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Content from Why Data Classification Matters

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Last updated on 2026-08-27 | Edit this page

Overview

Questions

• Why should we classify data instead of treating everything the same?
• What happens if classified data is breached?
• Who is responsible for data classification?

Objectives

• Understand why data classification is essential for research institutions
• Recognize the business, legal, and ethical stakes of data security failures
• Identify your role and responsibility in protecting research data

The Cost of a Data Breach

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Imagine this scenario: A biology lab at a mid-size research institution discovers that student genetic data from a research project (including names, phenotypes, and DNA markers) has been exposed on an unsecured server. The data wasn’t encrypted. File permissions were misconfigured. Nobody noticed until a journalist investigating data privacy called the institution with questions.

Callout

The Domino Effect of a Data Breach

What follows is: - Notification requirement: Alerting 200+ students of the breach - Regulatory investigation: FERPA violation inquiry from the U.S. Department of Education - Reputational damage: Loss of trust from students, families, and collaborators - Legal liability: Lawsuits from affected students - Operational costs: Incident response, forensics, notifications, credit monitoring - Compliance consequences: Corrective action plan, ongoing audits

This isn’t hypothetical. In 2019, over 500 million research records were breached across higher education institutions. The average cost per institution exceeded $2 million.

At Pomona College, we take a different approach: classify data before it becomes a problem.

The Three-Tier System

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Pomona College uses a simple but powerful framework: three tiers, each with specific handling requirements.

┌─────────────────────────────────────────────┐
│ PUBLIC                                      │
│ Encryption: NOT required                    │
│ Access: Unrestricted or shared with group   │
│ Example: Published papers, course materials,│
│          draft research shared with lab     │
└─────────────────────────────────────────────┘
         ↓
┌─────────────────────────────────────────────┐
│ PROPRIETARY                                 │
│ Encryption: RECOMMENDED                     │
│ Access: Restricted to authorized users      │
│ Example: Grant proposals, personnel data    │
└─────────────────────────────────────────────┘
         ↓
┌─────────────────────────────────────────────┐
│ RESTRICTED                                  │
│ Encryption: REQUIRED (AES-256-GCM)          │
│ Access: Strict controls + audit logging     │
│ Example: Student grades, CUI, export-ctrl  │
└─────────────────────────────────────────────┘

Why Classification Matters at Pomona

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Business Impact

Beyond compliance:

• Research integrity: Unauthorized access can compromise your research validity
• Competitive advantage: Unpublished research or grant proposals are competitive assets
• Institutional reputation: Pomona’s reputation for protecting data affects funding and partnerships
• Insurance coverage: Some data breaches aren’t covered if proper controls weren’t in place

Real-World Scenarios

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Scenario 1: The Published Paper Nobody Meant to Share

Dr. Sarah’s lab publishes a genomics study and deposits the processed dataset in a public repository (appropriately). But on Sagehen HPC, the raw sequencing files (which can re-identify study participants) were stored in a standard /bigdata directory with permissive file permissions. A summer intern accidentally shared access to the entire directory with a collaborator. The raw data is now partially exposed.

Problem: Raw data should have been classified as Restricted and encrypted.

Consequence: FERPA violation, breach notification, loss of future research funding.

Prevention: Classify data, implement proper access controls, encrypt Restricted data.

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Scenario 2: The Intercepted Grant Proposal

A graduate student transfers a draft grant proposal (marked “Proprietary”) from Sagehen to her laptop via SCP over an unencrypted connection, intending to work on it at home. The data is intercepted by someone on the same coffee shop WiFi. The competitor institution with whom you’re competing for the same grant now knows your approach.

Problem: Proprietary data wasn’t encrypted in transit.

Consequence: Lost funding opportunity, distrust from faculty advisor.

Prevention: Encrypt Proprietary data at rest and in transit; use secure channels like OneDrive+rclone.

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Scenario 3: The Oversharing AI Researcher

Dr. James uploads a dataset containing student survey responses to an AI model training service (in the cloud) to improve his analysis. The service’s terms indicate data can be used for model improvement and shared with other customers. He didn’t realize the data is Restricted (contains PII).

Problem: Restricted data left Pomona infrastructure without encryption and safeguards.

Consequence: Student privacy violation, compliance violation, potential legal action.

Prevention: Classify before transferring, de-identify Proprietary/Restricted data, get approval from ORSP/IRB.

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Scenario 4: The Helpful Graduate Student

A graduate student, tasked with managing her lab’s data, sets up a shared folder on her personal Dropbox account to make it easier for the team to access files from home. The folder contains draft papers, preliminary data, and some student contact information.

Problem: Confidential institutional data on an unmanaged personal cloud account.

Consequence: Data loss if account is compromised, potential compliance violation, loss of control.

Prevention: Use Pomona-managed Box or OneDrive with encryption; classify all data first.

Your Role and Responsibility

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Who Classifies Data?

The Principal Investigator (PI) is responsible for classifying research data before it goes on Sagehen. However:

• You must understand the classification system to implement it correctly
• Your PI sets policy for the lab’s data
• ITS Research Computing reviews classification for DOD-funded projects and CUI

What Happens If You Don’t Classify?

1. Default assumption: All unclassified data is treated as Restricted for compliance purposes
2. Access restriction: Overly conservative access controls may impede collaboration
3. Audit risk: Unclassified data triggers security reviews
4. Compliance violation: Failure to classify is itself a policy violation

Bottom line: Classify proactively or face more restrictive (and inconvenient) handling.

Incident Reporting

Callout

Report Security Incidents Immediately

If you suspect a data breach or security incident: 1. Stop working with the affected data immediately 2. Email its-hpc@pomona.edu with: - What you observed - When you discovered it - What data may be affected - Any access logs or evidence 3. Don’t investigate further; let ITS handle forensics

Reporting is not a punishment; it’s how we prevent bigger problems.

Challenge

Challenge 1.1: Cost Estimation

Research the cost of a data breach at a higher education institution. Consider: - Notification and credit monitoring - Legal fees and settlements - Lost funding due to compliance violations - Reputational damage

What’s the average cost to your institution per record breached?

Show me the solution

Typical answer: $200–$500 per record. For 1,000 records, that’s $200,000–$500,000 minimum — not including reputational damage, lost research funding, or regulatory sanctions.

Key Points

• Data classification is mandatory at Pomona College
• Pomona uses three tiers: Public, Proprietary, Restricted
• Restricted data requires encryption (AES-256-GCM) using gocryptfs
• Data breaches have significant business, legal, and reputational costs
• PIs are responsible for classification; you implement it correctly
• Incident reporting is your responsibility; contact its-hpc@pomona.edu immediately if compromised

Content from Regulations and Compliance

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Last updated on 2026-08-24 | Edit this page

Overview

Questions

• Which regulations apply to research at Pomona College?
• What are the consequences of regulatory violations?
• How do I know if my research falls under export control?

Objectives

• Learn about regulatory compliance requirements affecting Pomona College
• Understand FERPA, EAR/ITAR, and NIST SP 800-171 obligations
• Recognize which regulations apply to your research

Legal Compliance

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Pomona College is subject to multiple regulations that mandate data protection:

Callout

FERPA: Family Educational Rights and Privacy Act

• Protects student education records (grades, assessments, personal information)
• Violation risk: Loss of federal funding, lawsuits
• Your responsibility: Any research involving Pomona students falls under FERPA

Callout

EAR/ITAR: Export Administration Regulations / International Traffic in Arms Regulations

• Controls research with military applications or dual-use potential
• Covers: Cryptography, aerospace, semiconductors, biotech, materials science
• Violation risk: Federal criminal charges, loss of export privileges
• Your responsibility: Even basic research can become subject to export control; consult ORSP before publishing

Callout

NIST SP 800-171: Safeguarding Controlled Unclassified Information

• Required for any research involving Department of Defense funding
• Mandates specific security controls for CUI data
• Your responsibility: DOD-funded projects must meet 800-171 controls or lose funding

Callout

Pomona Information Security Policy

• Institutional requirement for all data stored on college systems
• Defines classification tiers and required controls
• Violation risk: Loss of computing privileges, dismissal

FERPA Data Classification

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Not all student data is Restricted. Here’s how FERPA classification works:

Data	Classification	Reason
Published study with anonymous aggregated student data	Public	No identification possible; fully public
Aggregated class grades (average GPA by major)	Public	No individual identification; shared within institution
List of students enrolled in BIOL 101 (with names but no grades)	Proprietary	Directory information, but protected by FERPA
Student grades linked to names or identifiers	Restricted	“Education record” under FERPA
Survey responses with student names or IDs	Restricted	Counts as education record if conducted by institution

Export Control: When Your Research Becomes Restricted

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Some research isn’t obviously restricted until analysis shows it might have export-control implications:

Chemistry Lab: Developing new catalysts for chemical synthesis - Pure research → Public/Proprietary (early stage, depending on sharing within group or restricted) - Novel catalyst with asymmetric properties → Discuss with ORSP - If applicable to pharmaceutical manufacturing or weapons → Restricted (EAR)

Computer Science Lab: Cryptography research - Academic paper on theoretical security proofs → Public (once published) - Implementation of novel encryption algorithm (before publication) → Restricted (EAR Category 5, Part 2) - Unclassified cryptographic software (even if open-source) → Restricted (EAR until published/reviewed)

Biology Lab: Genomics research - Published analysis of human genome variation → Public - Raw genomic data from participants → Restricted (FERPA + PII) - Research on pathogenic organisms → Restricted (CUI or potential dual-use concern)

Challenge

Challenge 2.1: Identify the Regulation

For each scenario, identify which regulation(s) apply: 1. A researcher stores student performance data for an education study 2. A lab works on cryptographic algorithms for secure communication 3. A faculty member manages payroll data for lab staff 4. A researcher analyzes publicly available Census data

Show me the solution

• 1. FERPA, Pomona ISP — student performance data is an education record
• 2. EAR/ITAR (if novel/advanced crypto), NIST 800-171 (if DOD-funded)
• 3. Pomona ISP — personnel data = Proprietary at minimum
• 4. None specific (Public); standard Pomona ISP applies

Key Points

• Multiple regulations (FERPA, EAR/ITAR, NIST 800-171) drive Pomona’s data policy
• FERPA protects student education records; violations risk federal funding
• Export controls (EAR/ITAR) apply to cryptography, advanced materials, and dual-use research
• NIST SP 800-171 governs DOD-funded research with CUI
• Pomona’s Information Security Policy applies to all data on college systems
• When in doubt about export control, consult ORSP before sharing or publishing

Content from The Three Tiers Overview

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Last updated on 2026-08-27 | Edit this page

Overview

Questions

• What distinguishes one classification tier from another?
• What security controls are required for each tier?
• What happens if I misclassify data?

Objectives

• Understand the three classification tiers and their defining characteristics
• Learn the security requirements for each tier
• Recognize examples from different research disciplines

Pomona’s Three-Tier Classification System

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Data classification at Pomona College follows a simple principle: the more sensitive the data, the more security controls it requires. The three tiers stack on top of each other; Restricted data includes all protections of Proprietary and Public.

Three tiers, and what each one obliges you to do.

Tier 1: PUBLIC

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Definition: Data that has been intentionally released for unrestricted use and is freely available to anyone, or data shared openly within Pomona College and authorized groups with no special risk of harm if accessed.

Characteristics

• Availability: Either publicly available worldwide, or freely shared within Pomona/lab groups
• Sensitivity: No harm if accessed by authorized lab members or the public
• Examples: Published research papers, public datasets, course materials, lab notes, draft research shared with lab members, operational data, institutional announcements
• Regulatory concern: None (unless containing other sensitive data)

Key Features

Aspect	Details
Encryption	Not required
Access controls	None required for public; group-readable (750) for internal shared data
Storage	Anywhere on Sagehen HPC (/rhome or /bigdata)
Sharing	Unrestricted (public) or with lab members (shared within groups)
Retention	No special policy
Audit logging	Not required
File permissions	755 (public) or 750 (group-shared)

Two Categories of Public Data

Fully Public: Released to the world - Published manuscripts and supplementary data - Open-source code repositories - Datasets in public repositories (Zenodo, OSF, figshare) - Institutional announcements and public reports

Shared Internal to Groups: Freely available within Pomona/lab - Lab notebooks and research journals (shared with group) - Preliminary analysis and working drafts (not yet published) - Operational data (schedules, lab protocols, meeting notes) - Course materials and lecture notes - Raw data during analysis phase (before publication)

Examples from Pomona Research

• Published manuscript: “Optimal metabolic pathway design using constraint-based modeling” published in Nature Biotechnology with data on GitHub
• Open-source code: Lab maintains public GitHub repository for molecular simulation software
• Course materials: Lecture notes, practice problems, and datasets for BIOL 101
• Lab notebook: Digital research journal shared among the lab (but not published)
• Data in progress: Raw measurements shared with lab members during analysis
• Institutional data: Published class schedule, faculty directory, graduation statistics
• Data from public repositories: Census data, genomics data from NCBI, weather data from NOAA

Safeguards (Still Important!)

Even though Public data needs no special protection: - Verify before sharing: Confirm data was actually approved for public release - Attribution: Respect intellectual property and attribution requirements - Version control: Clearly mark which version is public (some research evolves) - Metadata: Include proper documentation so others can use it correctly - Confidentiality agreements: Respect collaboration terms for internally shared data

Summary Table

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Requirement	PUBLIC (green)	PROPRIETARY (orange)	RESTRICTED (red)
Permissions	755	750	700 + gocryptfs
Encryption	Not required	Recommended	REQUIRED (AES-256-GCM)
Audit logging	Not required	Optional	REQUIRED

All three tiers demonstrated: directories and files created and chmod-ed per tier — 755/644 PUBLIC, 750/640 PROPRIETARY, 700/600 RESTRICTED (which additionally requires gocryptfs). Note Sagehen HPC sets the setgid bit on new directories, so your ls -l will show an s where these examples show plain modes.

Permission variant on PROPRIETARY: a 600 (user-only) variant may be appropriate for sub-team-only or single-PI files within a PROPRIETARY workflow. It is a variant of PROPRIETARY handling, not a separate tier — encryption is still optional and the data still belongs to the orange tier. Files needing user-only and strong protection belong in RESTRICTED (700 + gocryptfs).

Callout

There Are Three Tiers, Not Four

Pomona’s classification model has exactly three tiers: PUBLIC, PROPRIETARY, RESTRICTED. There is no fourth “INTERNAL” tier. If you encounter “INTERNAL” in legacy materials, treat it as PUBLIC (intra-group sharing) or PROPRIETARY (sensitive lab work). The April 2026 standardization removed the fourth tier to eliminate ambiguity.

Challenge

Challenge 3.1: Classify Pomona Research Datasets (Part 1)

For each dataset, identify the appropriate classification and explain your reasoning: 1. BIOL 101 Course Data: - Student names, email addresses, class schedule - Published aggregated grade distribution (average GPA by major) 2. Economics Research: - Aggregated economic indicators from public Census data - Survey responses from 500 households (de-identified but with geographic location)

Show me the solution

1. Student names + email + class schedule → RESTRICTED (FERPA-protected education record). Note: student names alone may be directory information unless the student has opted out, but the moment they’re paired with academic records (class schedule, grades, course enrollment), the dataset becomes a FERPA-protected education record requiring RESTRICTED-tier handling (700 + gocryptfs). Aggregated grade distribution (no individual identification) → PUBLIC.
2. Census data → PUBLIC (already public, freely available); survey with location → PUBLIC (aggregated, de-identified) or PROPRIETARY if sensitive topics requiring restricted access

Key Points

• Public data is either fully publicly available or freely shared within lab/groups; no special handling required
• File permissions: PUBLIC=755 (canonical)
• Public data still requires attribution and version control
• Two categories exist: fully public and shared internal to groups
• Classification determines where data is stored and how it’s protected

Content from Identifying Data Types

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Last updated on 2026-08-24 | Edit this page

Overview

Questions

• How do I know if my data is Proprietary or Restricted?
• What makes data Restricted versus Proprietary?
• What are common examples of each tier?

Objectives

• Understand the Proprietary and Restricted tiers in detail
• Identify which tier applies to specific research data
• Recognize examples of Proprietary and Restricted data across disciplines

Tier 2: PROPRIETARY

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Definition: Data that provides competitive or institutional advantage and would cause harm if disclosed to unauthorized parties. Access is restricted to specific authorized individuals. Examples include unpublished research, grant proposals, sensitive personnel data, and data needing group-level access control beyond simple sharing.

Characteristics

• Availability: Restricted to specific authorized individuals (may be broader than one person, but not unrestricted)
• Sensitivity: Loss or disclosure would cause business/institutional/competitive harm; or contains sensitive information requiring controlled access
• Examples: Grant proposals, unpublished research findings, patent disclosures, business plans, salary information, data requiring departmental/group-level access controls
• Regulatory concern: Varies; may involve intellectual property or institutional policy

Key Features

Aspect	Details
Encryption	Recommended, especially for sensitive proprietary data
Access controls	User-level or group-level restrictions (specific individuals or authorized group members)
Storage	/rhome or /bigdata with 750 permissions (canonical PROPRIETARY)
Sharing	Restricted; requires explicit authorization
Retention	Until publication or as specified by PI
Audit logging	Recommended for highly sensitive data
Transfers	Use secure channels; encryption recommended
File permissions	750 (canonical PROPRIETARY). A 600 (user-only) variant is acceptable for sub-team-only files but is a variant of PROPRIETARY handling, not a separate tier.

Examples from Pomona Research

• Grant proposal (pre-award): NSF CAREER proposal being prepared; not yet submitted
  • Why Proprietary? Competitive; other labs might be pursuing similar ideas
  • Classification changes: Becomes Public after award announcement
• Patent disclosure: Novel data suggesting patentable methodology
  • Why Proprietary? Patent novelty requires non-disclosure until filing
  • Classification changes: Becomes restricted once patent filed (legal privilege)
• Unpublished novel findings: Preliminary data showing unexpected biological phenomenon
  • Why Proprietary? Competitive advantage if first to publish
  • Classification changes: Becomes Public once published
• Industry collaboration data: Preliminary results from partnership with a biotech firm
  • Why Proprietary? Contractual confidentiality clause
  • Classification changes: Determined by collaboration agreement
• Personnel data: Staff salaries, performance reviews, contact information
  • Why Proprietary? Privacy and institutional policy
  • Classification changes: Remains Proprietary indefinitely (or moves to Restricted if PII)

Callout

Encryption Recommendation for Proprietary Data

While not required, encryption is strongly recommended for Proprietary data because: - Grant proposals are highly valuable - Unencrypted data on shared clusters can be breached (misconfigured permissions) - Intellectual property loss has lasting consequences - Encryption requires minimal effort with modern tools

Tier 3: RESTRICTED

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Definition: Data that is strictly confidential and requires the highest level of protection. Unauthorized access could result in legal liability, regulatory violation, privacy harm, or security risk. Encryption is mandatory.

Characteristics

• Availability: Strictly limited to authorized individuals; audit trails required
• Sensitivity: Disclosure would violate law, regulation, or privacy rights
• Examples: Student education records (FERPA), export-controlled research (EAR/ITAR), personally identifiable information (PII), health information (HIPAA), Controlled Unclassified Information (CUI)
• Regulatory concern: High; violations can result in federal penalties

Key Features

Aspect	Details
Encryption	REQUIRED (AES-256-GCM via gocryptfs)
Access controls	Strict user-level controls; specific authorization required
Storage	/rhome or /bigdata in encrypted container
Sharing	Extremely restricted; institutional/legal approval required
Retention	Governed by regulation or legal requirement
Audit logging	REQUIRED; all access must be logged
Transfers	Prohibited without encryption in transit; approved channels only
Destruction	Secure deletion required at end of retention period
File permissions	700+gocryptfs (encrypted container with strict access)

Examples from Pomona Research

FERPA-Protected Data (Student Education Records) - Student grades from a research study - Student names linked to academic performance data - Email addresses combined with performance metrics - Encryption: REQUIRED - Access: Only researchers and authorized staff

Export-Controlled Research (EAR/ITAR) - Advanced materials research potentially useful for military applications - Cryptographic algorithms and implementations - Semiconductor design specifications - Biotech dual-use research of concern (DURC) - Encryption: REQUIRED - Access: Only authorized personnel (typically U.S. citizens on approved projects)

Personally Identifiable Information (PII) - Names + social security numbers - Names + financial information (bank accounts, credit cards) - Genetic data linked to identifiable information - Medical records - Encryption: REQUIRED - Access: Only individuals with explicit need-to-know

Controlled Unclassified Information (CUI) - Research data funded by Department of Defense - Cybersecurity/critical infrastructure research - Biological agents research data - Encryption: REQUIRED - Audit logging: REQUIRED

Challenge

Challenge 4.1: Classify Pomona Research Datasets (Part 2)

For each dataset, identify the appropriate classification and explain your reasoning: 1. Neuroscience Lab Dataset: - fMRI brain scans from participants (no names, coded with ID numbers) - Demographic data: age, gender (de-identified, statistical summary only) - Raw brain images linked to participant ID codes 2. Chemistry Lab Dataset: - Synthesis procedures for novel catalysts (before publication) - Final published paper in Nature Chemistry with all data

Show me the solution

1. fMRI scans without names → Proprietary (valuable research asset); demographic summary → Public (aggregated, shared within group); raw images linked to IDs → Restricted (re-identification risk)
2. Pre-publication procedures → Proprietary (competitive); published paper → Public

Key Points

• Proprietary data is restricted to specific authorized individuals; encryption recommended
• Restricted data requires mandatory encryption (AES-256-GCM via gocryptfs) and audit logging
• File permissions: PUBLIC=755, PROPRIETARY=750 (canonical; 600 variant for sub-team-only files), RESTRICTED=700+gocryptfs
• FERPA, EAR/ITAR, CUI, and PII data are automatically Restricted
• When in doubt, classify conservatively and ask your PI

Content from The Classification Process

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Last updated on 2026-08-27 | Edit this page

Overview

Questions

• How do I classify my specific research datasets?
• What information do I need to make good classification decisions?
• When should I ask for help?

Objectives

• Apply Pomona’s classification system to your own research data
• Use the decision tree to classify datasets systematically
• Recognize when to consult your PI or ORSP

The Classification Workflow

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Classifying data is not a one-time event; it’s an ongoing process as your research evolves. Here’s the workflow:

1. Inventory your data
         ↓
2. Understand the content
         ↓
3. Apply classification rules
         ↓
4. Implement handling requirements
         ↓
5. Monitor and re-classify as needed

The same four questions, answered differently for each tier.

Step 1: Inventory Your Data

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Before you can classify, you need to know what you have.

Creating a Data Inventory

For each research project or lab, list:

Item	Example
Project name	“Human gut microbiome and metabolic syndrome”
Data type	Raw sequencing reads, metadata, analysis results
Data location	/bigdata/lab/<labname>/project_A
Size	5 TB raw data
Source	Sequencing facility, clinical partner institution
People who need access	PI (Dr. Smith), postdoc (Jane), graduate students (Tom, Maria)
Retention requirement	7 years (NIH requirement)
Special handling	IRB-approved study with human subjects

Callout

Sample Inventory Template

Lab Data Classification Inventory
==================================

Project: [Project Title]
PI: [PI Name]
Date: [Date]

Dataset 1: [Name]
├─ Description: [What is this data?]
├─ Size: [GB/TB]
├─ Source: [Where did it come from?]
├─ Contains PII? [Yes/No]
├─ Contains export-controlled tech? [Yes/No]
├─ Student data? [Yes/No]
├─ Health data? [Yes/No]
├─ Current classification: [Public/Proprietary/Restricted]
└─ Reason: [Why this classification?]

Dataset 2: [Name]
├─ [Same fields...]

Use this template to document your data inventory for audit purposes.

Step 2: Understand the Content

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Classification depends on understanding what your data contains and implies, not just its format.

Key Questions to Ask

Dimension	Question	Implication
Origin	Published repository?	Likely Public
	Human subjects study?	Likely Restricted if identifiable
	DOD-funded?	Likely Restricted (CUI)
Content	Contains names/IDs linked to sensitive info?	Restricted
	Contains health data with identifiers?	Restricted (HIPAA)
	Contains export-controlled tech?	Restricted (EAR)
	Aggregated/anonymized?	Public or Proprietary
Context	Pre-publication competitive asset?	Proprietary
	Already published?	Public
	Privacy/legal harm if disclosed?	Restricted

Step 3: Apply Classification Rules

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Use the decision tree systematically.

The Systematic Approach

Rule 1: Check for Regulatory Triggers (Automatic Restricted)

Is ANY of the following true?
├─ Contains student education records? → RESTRICTED (FERPA)
├─ Contains health information linked to individuals? → RESTRICTED (HIPAA)
├─ Contains export-controlled research? → RESTRICTED (EAR/ITAR)
├─ Contains CUI or DOD-funded data? → RESTRICTED (NIST 800-171)
└─ If YES to any → **STOP. CLASSIFY AS RESTRICTED.**
   If NO → Continue to Rule 2.

Rule 2: Check for Proprietary Triggers

Is ANY of the following true?
├─ Contains PII or sensitive personal information? → PROPRIETARY (min)
├─ Unpublished novel findings (competitive asset)? → PROPRIETARY
├─ Grant proposal, patent, or IP disclosure? → PROPRIETARY
├─ Sensitive institutional data (salary, strategy)? → PROPRIETARY
└─ If YES to any → Classify as PROPRIETARY (or Restricted if also regulatory trigger)
   If NO → Continue to Rule 3.

Rule 3: Check for Public Triggers

Is ANY of the following true?
├─ Already published or intended for public use? → PUBLIC
├─ Freely shared within lab group? → PUBLIC
├─ Preliminary or in-progress data (shared with group)? → PUBLIC
├─ Data with no access restrictions? → PUBLIC
└─ If YES to any AND no regulatory/proprietary concerns → Classify as PUBLIC

Challenge

Challenge 5.1: Classify Your Own Data

Using the systematic approach from this episode: 1. Choose a dataset from your own research 2. Apply the three rules (Regulatory → Proprietary → Public) 3. Document your reasoning 4. Identify anyone who needs access 5. Note any questions/escalations needed

Show me the solution

Here is an example for a fictional genomics dataset:

DATASET: Sagehen Meadow Vole Whole-Genome Sequences

Regulatory Check:
- FERPA applies?  No (no student education records)
- HIPAA applies?  No (non-human species)
- Export-control applies?  No (basic ecological research)
- CUI applies?  No (NSF-funded, no DOD involvement)

Proprietary Check:
- Contains PII?  No (wildlife data, no human subjects)
- Novel unpublished findings?  Yes — first population genomics
  study of meadow voles in the Sagehen basin
- Competitive asset?  Yes — two other labs studying the same
  species; first-to-publish advantage

Classification Decision:
- Tier: PROPRIETARY
- Reasoning: No regulatory triggers, so not Restricted. However,
  the dataset contains novel unpublished findings with competitive
  value. Classify as Proprietary until the manuscript is accepted
  for publication, then reclassify to Public and deposit in NCBI SRA.

Key Points

• Inventory your data: know what you have before classifying
• Understand content: ask whether your data contains PII, health info, export-controlled tech
• Apply rules systematically: check Regulatory triggers first, then Proprietary, then Public
• Classification is not a one-time event; re-classify as research evolves
• Ask for help: consult PI, ORSP, or ITS when uncertain

Content from Decision Framework

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Last updated on 2026-08-27 | Edit this page

Overview

Questions

• How do I document my classification for the audit trail?
• What are the most common classification mistakes?
• How do I handle reclassification as research evolves?

Objectives

• Use the classification decision tree to classify any dataset
• Document classification decisions for auditing
• Recognize common classification mistakes and how to avoid them

Classification Decision Tree

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Use this flowchart to classify your data:

START: New dataset or file

│
├─> Does it contain student education records?
│   (grades, performance data, assessment results linked to students)
│   YES → RESTRICTED (FERPA)
│   NO → Continue
│
├─> Does it contain legally protected health information?
│   (names + medical conditions, health data, genetic data)
│   YES → RESTRICTED (HIPAA/Privacy)
│   NO → Continue
│
├─> Does it contain export-controlled research?
│   (cryptography, advanced materials, dual-use bio research)
│   YES → RESTRICTED (EAR/ITAR)
│   UNCERTAIN → Consult ORSP
│   NO → Continue
│
├─> Does it contain CUI or DOD-funded research data?
│   YES → RESTRICTED (NIST 800-171)
│   NO → Continue
│
├─> Does it contain any personally identifiable information?
│   (names, SSN, financial info, genetic data, sensitive personal info)
│   YES → PROPRIETARY (minimum; may be Restricted if sensitive)
│   NO → Continue
│
├─> Does it contain sensitive institutional data?
│   (unpublished research, grant proposals, business plans, salaries)
│   YES → PROPRIETARY
│   NO → Continue
│
├─> Is it restricted to specific authorized individuals?
│   (only certain people can access, not the entire group)
│   YES → PROPRIETARY
│   NO → PUBLIC (freely available or shared openly within lab/groups)

Stop at the first question you answer yes to.

Decision Documentation

---

Once you’ve classified, document your decision. This serves two purposes: 1. Audit trail: Shows you thought carefully about classification 2. Consistency: Helps team members re-classify correctly if you leave

Callout

Classification Decision Form

DATA CLASSIFICATION DECISION RECORD
===================================

Project: [Name]
Date: [Date]
Classified by: [Name]

Dataset: [Dataset name/description]
Location: /bigdata/lab/<labname>/project_X

CLASSIFICATION DECISION: [PUBLIC / PROPRIETARY / RESTRICTED]

CLASSIFICATION REASONING: [Which rules triggered this classification]
REGULATORY FRAMEWORKS: [FERPA / HIPAA / EAR-ITAR / NIST 800-171 / Pomona ISP]
HANDLING: [Encryption / Access controls / Audit logging / De-id plan / Retention]
PEOPLE WHO NEED ACCESS: [Name/Role list]
RETENTION: [Until publication / 7 years / indefinite / etc.]
QUESTIONS: [Uncertainties requiring PI/ORSP review]

Store classification records in a shared lab document (e.g., /bigdata/lab/<labname>/DATA_CLASSIFICATION_LOG.md), accessible to your PI, updated when classification changes, and reviewed annually.

When to Ask for Help

---

• Consult your PI when uncertain about export control, collaboration agreements, human subjects, external sharing, or reclassification
• Consult ORSP for export control questions, IRB/FERPA applicability, CUI handling, or dual-use research concerns
• Consult ITS (its-hpc@pomona.edu) for classification decisions, encryption setup, access controls, or incident reporting

Common Misclassifications

---

Too High (Overly Restrictive)

Scenario: A researcher classifies all lab data as Restricted because she thinks “better safe than sorry.”

Problem: - Impedes legitimate collaboration - Creates unnecessary encryption overhead - Leads to audit fatigue (too much data flagged for review)

Fix: Use the decision tree; be precise about what’s actually sensitive.

Too Low (Inadequately Protective)

Scenario: A researcher classifies student survey data as Public because it’s “only shared with the lab.”

Problem: - Violates FERPA (student data is Restricted) - Creates legal/compliance liability - Not discoverable during audit until breach occurs

Fix: Classify conservatively; when in doubt, ask your PI or contact its-hpc@pomona.edu

Classifying by Format, Not Content

Wrong: “All .xlsx files are Public because they’re spreadsheets”

Right: “This .xlsx contains student test scores, so it’s Restricted. That .xlsx contains lab budget summaries, so it’s Public.”

→ Content and context matter, not file type.

Never Changing Classification

Wrong: “I classified this as Proprietary six months ago, so it stays Proprietary forever”

Right: “I classified this as Proprietary when it was a draft paper. Now it’s published, so it’s Public.”

→ Re-classify regularly as your research evolves.

Challenge

Challenge 6.1: Reclassification Scenario

A researcher is wrapping up her PhD project. Her data evolution: 1. Year 1 (Raw data): Classification → ? - Lab is generating new datasets from a collaborator’s samples - No publications yet - Shared among 5 lab members 2. Year 2 (Analysis): Classification → ? - Novel findings (unexpected mechanism) - Writing first manuscript - Preparing to submit to Nature 3. Year 3 (Publication): Classification → ? - Paper is published in open-access journal - Lab publishes data on public repository (GitHub)

For each stage, determine: - What is the appropriate classification? - What changed from previous stage? - What handling requirements apply?

Show me the solution

1. Public (shared freely within group during raw data stage)
2. Proprietary (novel findings, competitive advantage during manuscript review)
3. Public (published and deposited in public repository)

Re-classification required at each transition!

Challenge

Challenge 6.2: Real-World Classification

A computer science researcher is working on a deep learning model for medical image analysis. Her dataset includes: - 10,000 chest X-ray images - Patient age and gender (de-identified with code numbers) - Radiologist diagnoses (labeled as positive/negative for pneumonia)

She wants to: 1. Share the dataset with a collaborator at another university 2. Upload the dataset to a cloud service for training models 3. Publish the dataset for reproducibility

For each action, determine: - What classification does this data require? - What are the barriers to this action? - What would you recommend?

Show me the solution

Classification: Restricted (health information + potential re-identification from image metadata + diagnosis data)

Barriers: - HIPAA-covered entity (if hospital data) - Re-identification risk (even de-identified X-rays can identify individuals) - Legal/ethical constraints on external sharing

Recommendations: 1. Collaborator sharing: Collaboration agreement required; encrypted transfer; access to encrypted instance only 2. Cloud training: Use DEA or BAA; encrypt data in transit/at rest; consider federated learning instead 3. Publishing: Publish only heavily de-identified version (aggregated results); keep raw data restricted

Key Points

• Use the decision tree to classify systematically: check regulatory triggers first
• Document decisions: create an audit trail for classification choices
• Re-classify regularly: as research evolves (publication, sharing, retention end)
• Avoid common mistakes: classifying by format, over-classifying, or never reclassifying
• Implement requirements: classification is meaningless without proper handling

Content from Handling PUBLIC Data

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Last updated on 2026-08-27 | Edit this page

Overview

Questions

• What access controls are needed for Public data?
• Where should Public data be stored on Sagehen?
• What permissions should I set for Public files?

Objectives

• Understand the specific handling requirements for PUBLIC data
• Implement access controls appropriate to Public data
• Set up storage for Public data on Sagehen HPC

PUBLIC Data Handling Requirements

---

Access Control

Who can access: Anyone (unrestricted) or lab group (shared)
Default file permissions: world-readable (755) or group-readable (750)

No special access control is needed. Public data is by definition available to the world or freely shared within groups.

Storage

• Location: Anywhere on Sagehen (/rhome or /bigdata)
• Encryption: Not required (not recommended; unnecessary overhead)
• File system: Standard Sagehen filesystem

Example: Published Dataset

BASH

# Published paper's supplementary data
/bigdata/lab/<labname>/published_paper_2025/
  ├── README.md (DOI, citation info)
  ├── data_clean.csv
  ├── analysis.R
  └── figures/

# Permissions (world-readable)
ls -l /bigdata/lab/<labname>/published_paper_2025/
-rw-r--r-- 1 pi_user lab_group  500M data_clean.csv
-rw-r--r-- 1 pi_user lab_group   50K analysis.R

Audit Logging

Not required for Public data.

Retention

No special requirements. Retain as long as useful for reproducibility (typically as long as the paper is cited, indefinitely for reproducibility).

Storage Layout for Public Data

---

Fully Public Data

BASH

/bigdata/lab/<labname>/public_datasets/
├── published_paper_data/
│   ├── data_clean.csv
│   └── analysis.R
└── course_materials/
    ├── lecture_notes.pdf
    └── sample_data.csv

# Permissions (world-readable)
chmod 644 /bigdata/lab/<labname>/public_datasets/*

Public Data Shared within Group

BASH

/bigdata/lab/<labname>/projects/
├── exp_2025_01/
│   ├── raw_data.h5
│   └── analysis/
└── exp_2025_02/
    ├── images/
    └── processed/

# Permissions (group-readable, 750)
chmod 750 /bigdata/lab/<labname>/projects/
find /bigdata/lab/<labname>/projects/ -type f -exec chmod 640 {} \;

Practical Exercise: Set Up Public Data Access

---

BASH

# Check your lab group
groups

# Set directory permissions for Public shared data
chmod g+rx /bigdata/lab/<labname>/public/
find /bigdata/lab/<labname>/public/ -type f -exec chmod 640 {} \;
find /bigdata/lab/<labname>/public/ -type d -exec chmod 750 {} \;

# Verify
ls -ld /bigdata/lab/<labname>/public/
ls -l /bigdata/lab/<labname>/public/data.csv

Challenge

Challenge 7.1: Public Data Setup

You have two datasets to store: 1. Published paper data (CSV, 5 GB) – fully public 2. Lab meeting notes (Markdown, 5 MB) – shared with lab group

For each: - Choose the storage location (/rhome or /bigdata) - Set the correct permissions - Explain whether encryption is needed

Show me the solution

1. Published data → /bigdata (large dataset) → permissions 755 → no encryption needed
2. Lab meeting notes → /bigdata (shared with group) → permissions 750 → no encryption needed

Key Points

• Public data: World-readable (755) or group-readable (750); no encryption needed
• Use /bigdata for large public datasets; /rhome is acceptable for small files
• No audit logging or special retention required for Public data
• Still verify data was approved for public release before sharing

Content from Handling PROPRIETARY Data

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Last updated on 2026-08-27 | Edit this page

Overview

Questions

• Who can access Proprietary data, and how do I enforce access?
• What’s the difference in handling between Proprietary and Restricted?
• How do I share Proprietary data with external collaborators?

Objectives

• Understand the specific handling requirements for PROPRIETARY data
• Implement access controls for Proprietary data on Sagehen HPC
• Manage external sharing of Proprietary data securely

PROPRIETARY Data Handling Requirements

---

Access Control

Access: Specific authorized individuals or designated group members (not all lab members) Default file permissions: owner only (600) or group-restricted (750)

BASH

# Proprietary lab data (grant proposals, unpublished research, restricted datasets)
/bigdata/lab/<labname>/proprietary/
  ├── grant_proposal_2025/
  │   ├── proposal_draft.pdf
  │   └── budget_narrative.doc
  └── unpublished_findings_2024/
      ├── data_analysis.xlsx
      └── manuscript_draft.docx

# Example 1: Permissions for PI-only access
ls -l /bigdata/lab/<labname>/proprietary/
drwx------ 5 pi_user pi_user   500K grant_proposal_2025/
-rw------- 1 pi_user pi_user   500K proposal_draft.pdf

# Example 2: Permissions for restricted group access (e.g., senior lab members only)
drwxr-x--- 5 pi_user senior_group   400K unpublished_findings_2024/
-rw-r----- 1 researcher senior_group  200M data_analysis.xlsx

Setting Up Proprietary Access

Step 1: Identify authorized users

Work with your PI to list who needs access: - PI - Postdocs directly involved - Specific grad students (not entire group) - External collaborators (rare; requires explicit approval)

Step 2: Set permissions

BASH

# For PI-owned files (PI is owner)
chmod 600 /bigdata/lab/<labname>/proprietary/grant_proposal_2025/*
ls -l /bigdata/lab/<labname>/proprietary/grant_proposal_2025/
-rw------- 1 pi_user pi_user ...

# For co-owned files (PI and one other person)
# Option A: PI grants explicit access via shared directory
mkdir /bigdata/lab/<labname>/proprietary/grant_proposal_2025/shared_draft/
chmod 700 /bigdata/lab/<labname>/proprietary/grant_proposal_2025/shared_draft/

# Option B: Use encryption (recommended; see Episode 10)

Storage Options

Option A: Unencrypted + restrictive permissions (for less sensitive proprietary data)

Location: /bigdata with 600 permissions
Encryption: Not required
Advantages: Fast, easy
Disadvantages: Vulnerable to local privilege escalation

Option B: Encrypted (recommended for sensitive proprietary data)

Location: /rhome or /bigdata with gocryptfs
Encryption: AES-256-GCM
Advantages: Resistant to breach even if permission misconfiguration occurs
Disadvantages: Slight performance overhead (see Episode 10)

Audit Logging

Optional but recommended for sensitive proprietary data: - Track who accessed which files - Useful if breach is suspected - Can detect unauthorized access attempts

Handling External Sharing

If you need to share Proprietary data with external collaborators:

1. Collaboration agreement: Ensure written agreement exists
2. Encryption in transit: Use SSH/SFTP, never plain FTP
3. Controlled access: Time-limit access; revoke when done
4. Audit trail: Log who accessed what

Example:

BASH

# Create temporary access for collaborator (valid 30 days)
mkdir /bigdata/lab/<labname>/shared_with_external/collaborator_name/
# Add access controls...
# Set calendar reminder to revoke access in 30 days

Retention

Determined by use case: - Grant proposals: Until award decision + 1 year - Unpublished research: Until publication (then reclassify to Public) - Patent disclosures: Until patent filed or decision made not to file - Personnel records: Per HR policy (typically 5-7 years)

Practical Exercise: Set Up Proprietary Data Access

---

BASH

# Create proprietary directory
mkdir -p /bigdata/lab/<labname>/proprietary/grant_proposal_2025

# Set permissions: owner only
chmod 700 /bigdata/lab/<labname>/proprietary/
chmod 600 /bigdata/lab/<labname>/proprietary/grant_proposal_2025/*

# Verify
ls -ld /bigdata/lab/<labname>/proprietary/
ls -l /bigdata/lab/<labname>/proprietary/grant_proposal_2025/

Challenge

Challenge 8.1: Set Up Proprietary Access Controls

Scenario: You have a lab with: - Draft grant proposal (PI-only access) - Unpublished research findings (PI + 2 grad students)

For each: 1. Identify the appropriate file permissions 2. Describe who can access what 3. Explain how you’d enforce access (what commands)

Show me the solution

Grant proposal (PI-only): - Permissions: 600 (owner only) - Who can access: PI only - Enforcement: chmod 600 grant_proposal/*; only PI owns the files

Unpublished findings (PI + 2 grad students): - Permissions: 750 (group-restricted) - Who can access: PI + approved grad students via a Unix group - Enforcement: chgrp approved_group unpublished_findings/ + chmod 750; group retains access control

Key Points

• Proprietary data: Group-readable (750) or owner-only (600); optional encryption for sensitive assets
• Access control is enforced through file permissions, not trust
• External sharing requires collaboration agreement + secure transfer + time-limited access
• Retention varies by use case; reclassify to Public after publication

Content from Handling RESTRICTED Data

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Last updated on 2026-08-24 | Edit this page

Overview

Questions

• What security controls are mandatory for Restricted data?
• How do I set up audit logging for compliance?
• How long must I retain data, and how do I delete it securely?

Objectives

• Understand the specific handling requirements for RESTRICTED data
• Implement mandatory encryption and audit logging
• Manage access authorization lists and secure deletion

RESTRICTED Data Handling Requirements

---

Callout

RESTRICTED Data: Mandatory Controls

Access Control

Access: Specific authorized individuals (strict) Audit logging: REQUIRED Default file permissions: owner only (700) + encrypted container

BASH

# Restricted data (must be in encrypted container)
/rhome/<myusername>/encrypted_restricted/  # gocryptfs mount point

# Cannot be accessed without:
# 1. Correct passphrase to mount the encrypted container
# 2. User must be specifically authorized

Storage (Mandatory Encryption)

Restricted data MUST be stored in an encrypted container. Standard permissions are not sufficient.

All Restricted data lives in a gocryptfs encrypted container
├─ Passphrase protects access (AES-256-GCM)
├─ Mounted at user's request
├─ Unmounted when not in use
└─ Requires authorization to mount

Setting Up Restricted Access

Step 1: Create encrypted container (see Episode 10 for full details)

BASH

# Initialize encrypted container
gocryptfs-init /bigdata/lab/<labname>/restricted_encrypted_base

# Creates:
# /bigdata/lab/<labname>/restricted_encrypted_base/ (encrypted files)
# /rhome/<myusername>/.config/gocryptfs/... (passphrase storage)

Step 2: Mount when needed

BASH

# Mount encrypted container
gocryptfs /bigdata/lab/<labname>/restricted_encrypted_base /rhome/<myusername>/restricted

# Encrypted data is now accessible
cd /rhome/<myusername>/restricted/
ls -l

Step 3: Access control within encrypted container

Even within encrypted container, use file permissions:

BASH

# Within mounted encrypted container
/rhome/<myusername>/restricted/
  ├── student_data/
  │   └── survey_responses_2024/
  │       ├── approved_researcher_1/ (readable by researcher_1 only)
  │       └── approved_researcher_2/ (readable by researcher_2 only)

# Permissions within encrypted container
-rw------- 1 researcher_1 lab_group
-rw------- 1 researcher_2 lab_group

Audit Logging (REQUIRED)

---

Every access to Restricted data must be logged.

Option A: Manual access log (simplest)

BASH

# Before accessing restricted data, log it
date >> /rhome/<myusername>/RESTRICTED_ACCESS_LOG.txt
echo "User: $(whoami), File: survey_data.csv, Action: READ" \
  >> /rhome/<myusername>/RESTRICTED_ACCESS_LOG.txt

# Example entry:
# Thu Mar 06 14:23:45 PDT 2025
# User: jane_postdoc, File: student_grades.csv, Action: READ

Option B: Automated logging (recommended)

BASH

# Set up automatic audit via auditd (with ITS help)
# Logs all reads/writes to encrypted container
auditctl -w /rhome/<myusername>/restricted/ -p rwa -k restricted_access_log

# View logs:
ausearch -k restricted_access_log

Option C: SFTP access logging (for transfers)

BASH

# If sharing Restricted data via SFTP, logs are automatic
# Review in /var/log/auth.log or server logs

Who Can Access Restricted Data?

---

Create an explicit access list documenting: the dataset name and classification, each authorized user (name, role, scope of access, who approved), unauthorized categories (e.g., summer interns not approved by IRB), and revocation triggers (user leaves lab, project ends, annual review expires). Store this alongside your classification decision record.

Handling Transfers of Restricted Data

---

Never transfer Restricted data unencrypted. Use: (A) copy within the encrypted container, (B) SCP/SFTP over SSH, or (C) Pomona-managed Box/OneDrive with encryption. Never use plain FTP, email attachments, unencrypted USB drives, or personal cloud storage.

Retention and Secure Deletion

---

Retention Requirements

Type	Retention	Reason
FERPA student records	3-7 years (per FERPA)	Education records
HIPAA health data	6 years minimum (per HIPAA)	Health information
CUI/DOD data	Per contract (typically 5-7 years)	NIST 800-171 requirement
Research data	Per funding agency (typically 3-7 years)	NIH, NSF requirements
Export-controlled	Until publication or declassification	EAR/ITAR requirement

Document retention requirements in your classification record.

Secure Deletion

At end of retention period, data must be securely deleted (not just removed):

BASH

# Insecure deletion (Don't do this!)
rm /path/to/restricted/data.csv
# File is removed from directory but recoverable from disk

# Secure deletion (Do this!)
shred -vfz /path/to/restricted/data.csv
# Overwrites file with random data multiple times before deletion

Challenge

Challenge 9.1: Audit Logging Scenario

A researcher is working with FERPA-protected student data. For the next week: 1. Keep a manual access log every time you access the data 2. Record: date, time, user, file name, action (read/write/delete) 3. Review the log at week’s end

Example log entry:

2025-03-06 14:23:45 jane_postdoc survey_responses.csv READ
2025-03-06 15:10:22 jane_postdoc analysis.R WRITE
2025-03-07 09:15:10 jane_postdoc student_grades.csv READ

After one week: - How many accesses occurred? - Who accessed what? - Were there any unusual patterns?

Show me the solution

Here is an example completed one-week access log:

RESTRICTED DATA ACCESS LOG
===========================
Dataset: student_learning_survey_2025 (FERPA-protected)
Week of: 2025-03-03 to 2025-03-07

DATE                 USER           FILE                          ACTION
2025-03-03 09:12:33  jgarcia        survey_responses.csv          READ
2025-03-03 09:45:10  jgarcia        analysis_cleaning.R           WRITE
2025-03-04 10:05:22  jgarcia        survey_responses_clean.csv    WRITE
2025-03-05 11:30:00  jgarcia        survey_responses_clean.csv    READ
2025-03-06 09:42:33  jgarcia        regression_results.csv        WRITE
2025-03-07 13:15:08  jgarcia        survey_responses.csv          READ

Observations: - Total accesses: 6 entries across 5 working days - Who accessed: Only jgarcia (sole authorized analyst) - Pattern: READ operations at start of day, WRITE operations follow – normal analytical workflow - Recommendation: For ongoing work, set up automated auditd logging

Key Points

• Restricted data: Owner-only (700) PLUS mandatory encryption (AES-256-GCM via gocryptfs); audit logging required
• Every access to Restricted data must be logged for compliance
• Create explicit access authorization lists; review annually
• Retention and secure deletion are required by regulation; use shred not rm
• Never transfer Restricted data unencrypted; use SSH/SCP or institutional channels

Content from Storage and Encryption

---

Last updated on 2026-08-27 | Edit this page

Overview

Questions

• Where should each classification tier be stored on Sagehen?
• What is gocryptfs and why do we use it?
• How do I create and mount an encrypted container?
• How do I ensure encrypted data is backed up securely?

Objectives

• Understand Sagehen HPC storage options (/rhome vs. /bigdata)
• Learn when and how to use gocryptfs for encrypting Restricted data
• Implement encrypted containers for sensitive research data
• Manage encrypted volumes: mounting, unmounting, backup

Sagehen HPC Storage Architecture

---

Storage Options

Sagehen provides two main storage spaces for research data:

Sagehen HPC Cluster Storage
================================

/rhome                              /bigdata
├─ Home directories                ├─ Shared project storage
├─ ~100 GB per user                ├─ ~1 TB per lab
├─ Fast SSD                        ├─ Large capacity (BeeGFS)
├─ Backed up (6-month retention)   ├─ Not backed up
├─ Per-user quotas                 └─ Shared across group
└─ Good for: Small files, code
   Encrypted containers,           Good for: Raw data, analysis,
   active work                      large files, archives

Note: /rhome and /bigdata share a 1 TB quota on BeeGFS.

Choosing Storage Location

Data Tier	/rhome	/bigdata	Decision
Public	OK (if <100GB)	Preferred	Use /bigdata for large datasets
Proprietary	Preferred (PI-only)	OK with restricted access	Use /rhome for sensitive PI data
Restricted	Preferred (encrypted)	OK (encrypted)	Encrypt in either location

Encryption with gocryptfs

---

Pomona requires AES-256-GCM encryption for Restricted data. gocryptfs creates encrypted directories on the filesystem – like a virtual encrypted hard drive inside your normal file system. It is ideal because it requires no root access, uses per-directory encryption, and performs well for HPC workflows.

Step 1: Create an Encrypted Container

BASH

cd /rhome/<myusername>/

# Create directory structure
mkdir -p encrypted_base    # Encrypted files (unreadable without passphrase)
mkdir -p restricted        # Mount point (where files appear decrypted)

# Initialize encrypted container
gocryptfs -init encrypted_base/

# When prompted, choose:
# 1: AES-256-GCM (Recommended)

When prompted for password:

DO:
✓ Use strong password (14+ characters per NIST SP 800-63B)
✓ Store password securely (password manager, not file)
✓ Make it unique (not your Pomona password)

DON'T:
✗ Use weak password (password, 123456, etc.)
✗ Store password in a file
✗ Share password with anyone

Step 3: Mount the Encrypted Container

BASH

gocryptfs /rhome/<myusername>/encrypted_base /rhome/<myusername>/restricted
# Enter your passphrase when prompted

# Work with files as usual
cd /rhome/<myusername>/restricted/
ls -l
cp ~/analysis.csv ./results/

Step 4: Unmount When Done

BASH

fusermount -u /rhome/<myusername>/restricted/

# Verify unmount
ls /rhome/<myusername>/restricted/
# (Directory is empty; files are inaccessible without passphrase)

Backup and Password Management

---

Never backup the unencrypted contents! Always unmount first, then back up the encrypted container:

BASH

fusermount -u /rhome/<myusername>/restricted/
tar czf /bigdata/lab/<labname>/backups/restricted_backup_2025_03.tar.gz \
    /rhome/<myusername>/encrypted_base/

Password storage (ranked by security): (1) Password manager (Bitwarden, 1Password, KeePass); (2) GPG-encrypted file; (3) Never use plain text files, email, or cloud storage.

Troubleshooting: If “password wrong,” check for typos and verify the correct container directory. If “already mounted,” run fusermount -u /path/. If quota is exhausted, move the encrypted container to /bigdata.

Challenge

Challenge 10.1: Set Up Encrypted Container

1. Create an encrypted container for Restricted data
2. Generate test data (create sample restricted files)
3. Unmount and verify files are inaccessible
4. Remount and verify files are recovered
5. Test backup: tar the encrypted_base directory

Document your process and any issues encountered.

Show me the solution

Step 1 – Create directories:

BASH

$ mkdir -p /rhome/$(whoami)/encrypted_base
$ mkdir -p /rhome/$(whoami)/restricted

Step 2 – Initialize gocryptfs:

BASH

$ gocryptfs -init /rhome/jgarcia/encrypted_base/
# Choose strong passphrase (14+ characters)
# The gocryptfs filesystem has been created successfully.

Step 3 – Mount and create test file:

BASH

$ gocryptfs /rhome/jgarcia/encrypted_base /rhome/jgarcia/restricted/
$ echo "This is test restricted data" > /rhome/jgarcia/restricted/test_file.txt

Step 4 – Verify encryption on disk:

BASH

$ ls /rhome/jgarcia/encrypted_base/
gocryptfs.conf  gocryptfs.diriv  ZxKr8Q2kNvE3pGtYwM7bLd

Step 5 – Unmount and verify:

BASH

$ cd ~
$ fusermount -u /rhome/jgarcia/restricted/
$ ls /rhome/jgarcia/restricted/
# (empty -- data is locked)

Step 6 – Remount and verify recovery:

BASH

$ gocryptfs /rhome/jgarcia/encrypted_base /rhome/jgarcia/restricted/
$ cat /rhome/jgarcia/restricted/test_file.txt
This is test restricted data

Step 7 – Test backup:

BASH

$ fusermount -u /rhome/jgarcia/restricted/
$ tar czf /rhome/jgarcia/encrypted_backup.tar.gz \
    -C /rhome/jgarcia encrypted_base/
$ tar tzf /rhome/jgarcia/encrypted_backup.tar.gz
encrypted_base/
encrypted_base/gocryptfs.conf
encrypted_base/gocryptfs.diriv
encrypted_base/ZxKr8Q2kNvE3pGtYwM7bLd

The backup contains only encrypted files – safe to store on unencrypted media.

Key Points

• Use /rhome for encrypted Restricted data (smaller, backed up); /bigdata for large encrypted data
• /rhome and /bigdata share 1 TB quota on BeeGFS
• gocryptfs provides AES-256-GCM encryption without root access
• Create encrypted container once, mount/unmount as needed
• Always unmount when not actively using Restricted data
• Back up the encrypted container, not the unencrypted contents
• Passphrase must be 14+ characters (NIST SP 800-63B); store securely, never share

Content from Sharing and Collaboration

---

Last updated on 2026-08-27 | Edit this page

Overview

Questions

• How do I share classified data with external collaborators?
• Can I use Google Drive or Dropbox for research data?
• Are there restrictions on sharing with international collaborators?
• How do I publish my data while protecting privacy?

Objectives

• Share data with collaborators while maintaining security
• Use institutional cloud storage (Box, OneDrive) with encryption
• Recognize international data transfer restrictions
• Write appropriate data availability statements

The Sharing Problem

---

Research is collaborative. You need to share data with PhD students at other universities, industry partners, international collaborators, funding agencies, journal reviewers, and other research groups. But you also need to protect classified data. This episode shows how to do both safely.

Classify before you share, not after someone asks.

Callout

Quick Reference: What Can Be Shared?

Classification	Can Share?	Restrictions
Public	Yes, freely	None
Proprietary	With permission	Requires collaboration agreement
Restricted	NO	Not without de-identification and approval

Core principle: Restricted data (FERPA, CUI, PII, export-controlled) cannot be shared in its original form.

Sharing Public Data

---

Can be shared: Freely, with anyone, any method

BASH

# Post on GitHub, Zenodo, OSF, or institutional repository
# Email directly
# Upload to shared cloud storage
# Include in paper supplementary materials

Sharing methods for data shared within groups: 1. Sagehen HPC direct access (collaborators get accounts) 2. Pomona-managed Box or OneDrive 3. Secure SFTP transfer via ITS 4. GitHub or public repository (if fully public)

Sharing Proprietary Data

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Sharing with External Collaborators

Requires: 1. Collaboration agreement (contract) 2. Secure transfer (encrypted) 3. Access controls at recipient’s site 4. Time-limited access

Transferring Proprietary Data Securely

Option A: Encrypted ZIP file (fast, simple)

BASH

zip -e proprietary_data.zip proposal_2025.pdf budget_narrative.doc
# Send ZIP via email, password via separate channel (phone call)

Option B: Pomona Box (institutional, recommended)

BASH

# Upload to Box with password + expiration
# Box provides HTTPS/TLS encryption, access controls, audit logging
rclone copy /rhome/<myusername>/data/ pomona_box:research/project_A/

Option C: SFTP via ITS Secure Transfer

BASH

# Request SFTP server access from ITS (its-hpc@pomona.edu)
sftp external_collaborator@secure.pomona.edu
put proprietary_data.tar.gz /drop_zone/

Sharing Restricted Data

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Core Rule: Cannot Share Directly

Restricted data cannot be shared with external parties in its original, identifiable form. If you need to share research findings based on restricted data, you must de-identify first (see Episode 12).

Using Institutional Cloud Storage

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Pomona Box (Recommended)

Box is Pomona’s institutional cloud storage with built-in encryption and access controls.

Box Security: - HTTPS/TLS encryption in transit - AES encryption at rest - Granular access controls (file-level) - Audit logging available - Compliant with FERPA, HIPAA standards

NOT for Restricted data (unless de-identified first).

What NOT to Use

Do NOT use for research data: - Google Drive, Dropbox, iCloud - AWS S3 (unless with specific institutional agreement) - Personal cloud accounts

Why: - No FERPA/HIPAA compliance - Data may be used for training AI models - No enterprise encryption standards - No audit logging

International Collaboration and Export Control

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If your research touches cryptography, advanced materials, biotech, or semiconductors, you may be subject to EAR/ITAR export controls.

U.S. persons (U.S. citizens, permanent residents)
    → Can access any data

Non-U.S. persons on approved projects
    → Can access with special license/agreement

Non-approved non-U.S. persons
    → CANNOT access export-controlled research

Before sharing with international collaborators:

1. Does my research involve novel cryptographic algorithms, advanced materials, semiconductor design, biological agents, or military applications?
2. If YES: Contact ORSP (orsp@pomona.edu) for approval
3. If UNCERTAIN: Consult ORSP anyway – better safe than violating federal law

Publishing Data in Repositories

---

Repositories suitable for Pomona research:

Repository	Suitable For	Access
Zenodo	General research data	Open
OSF	Multi-discipline projects	Open or restricted
NCBI	Biological/medical data	Open (de-identified)
GitHub	Code and some data	Public or private
Figshare	Figures, datasets	Open
Dryad	Data underlying publications	Open (curated)

Data Availability Statements

Required by most journals. Examples:

Public data:

"All data are publicly available at Zenodo
(https://doi.org/10.5281/zenodo.XXXXXXX)"

Restricted data:

"Data contain sensitive information (student records protected
under FERPA). De-identified data are available upon request.
Original data are available to qualified researchers under
data use agreement subject to IRB approval."

Challenge

Challenge 11.1: Sharing Decision Tree

For each dataset, decide the appropriate sharing method: 1. Published research paper data 2. Draft grant proposal 3. Student survey responses 4. Preliminary analysis (shared with lab) 5. Export-controlled simulation code

Show me the solution

1. Published research paper data → Public Zenodo deposit
2. Draft grant proposal → Encrypted transfer with collaboration agreement; Pomona Box with time-limited access
3. Student survey responses → De-identify + publish; keep encrypted original separate
4. Preliminary analysis → Sagehen direct access or Pomona Box with group access
5. Export-controlled code → ORSP approval required; can only share with approved U.S. persons or non-U.S. persons with deemed export license

Key Points

• Public data can be shared freely with anyone
• Proprietary data requires collaboration agreement + secure transfer
• Restricted data cannot be shared directly; must be de-identified first
• Use institutional Box/OneDrive; avoid personal cloud storage (Google Drive, Dropbox)
• Export-controlled research requires ORSP approval before international sharing
• Publish data with DOI for reproducibility and impact

Content from De-identification and Best Practices

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Last updated on 2026-08-27 | Edit this page

Overview

Questions

• What does “de-identification” mean, and when is it required?
• What techniques can I use to de-identify data?
• How do I publish my data while protecting privacy?
• What are the key best practices from this workshop?

Objectives

• De-identify and prepare data for external transfer or publication
• Apply de-identification techniques (removal, aggregation, generalization, perturbation)
• Publish data in repositories while protecting sensitive information
• Recognize course-wide best practices for data classification and handling

What Is De-identification?

---

De-identification removes (or obscures) information that could identify individuals, while preserving the research value.

Identifying Information

Identifiers that must be removed or obscured:

Category	Examples
Direct identifiers	Names, SSN, email, phone, address
Quasi-identifiers	Birth date, gender, ZIP code (in combination can re-identify)
Linkable identifiers	Medical record numbers, student ID numbers
Biometric data	Fingerprints, photographs, voice, DNA sequence
Sensitive attributes	Diagnosis, salary, grades (in combination can re-identify)

De-identification Techniques

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1. Removal (Safest for Publication)

BASH

# Before (identifiable student data):
name,student_id,major,gpa
Alice Johnson,S12345678,Biology,3.85
Bob Smith,S87654321,Chemistry,3.72

# After (de-identified):
student_id,major,gpa
1,Biology,3.85
2,Chemistry,3.72

2. Aggregation (Reduces Detail)

BASH

# Before (identifiable):
student,exam_score
Alice,92
Bob,87
Carol,95

# After (aggregated):
major,mean_score,std_dev,n
Biology,89.3,3.5,15
Chemistry,87.1,2.8,18

3. Generalization (Reduces Precision)

BASH

# Before (specific, re-identifiable):
birth_date,diagnosis
1989-03-15,Type 2 Diabetes

# After (generalized):
birth_year,diagnosis
1989,Type 2 Diabetes

4. Perturbation (Adds Noise)

BASH

# Before:
name,height_cm,weight_kg
Alice,165,58

# After (add random noise):
height_cm,weight_kg
166±2,59±3

De-identification Workflow

---

START: Identifiable research data (e.g., student survey)
   ↓
1. Identify what's identifiable
   (Names? IDs? Dates? Locations?)
   ↓
2. Choose de-identification method
   (Remove, aggregate, generalize, perturb?)
   ↓
3. Apply de-identification
   (Create new dataset from original)
   ↓
4. Verify no re-identification is possible
   (Could someone piece it back together?)
   ↓
5. Keep original (encrypted) separate from de-identified
   ↓
6. Share de-identified version

Publication Workflow

---

To publish data based on Restricted sources: (1) de-identify using the techniques above, (2) clean data and add documentation (README, metadata, codebook, license), (3) choose a repository (Zenodo, OSF, NCBI, etc.), (4) upload and receive a DOI, (5) cite the DOI in your paper. Always keep the original identifiable data encrypted and separate from the de-identified version. Store any mapping files (original ID to anonymous ID) in your gocryptfs encrypted container.

Course Summary

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You’ve completed Workshop 14: Data Classification and Handling. You can now:

• Classify research data using Pomona’s three-tier system (PUBLIC/PROPRIETARY/RESTRICTED)
• Implement security controls appropriate to each tier
• Store and encrypt Restricted data using gocryptfs (AES-256-GCM)
• Share data with collaborators while maintaining compliance
• De-identify sensitive data for publication
• Recognize and report data security incidents

Next Steps:

1. Inventory and classify your lab’s data: Use the classification worksheets
2. Set up encryption: Create gocryptfs containers for Restricted data
3. Document access controls: Create access authorization lists
4. Plan retention and deletion: Know when data can be discarded

For questions: - Data classification / Encryption / Storage: its-hpc@pomona.edu - Export control: orsp@pomona.edu - IRB/FERPA questions: orsp@pomona.edu

Additional Resources

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Pomona Documents: - Pomona Information Security Policy - HPC Usage Policy - Sagehen HPC User Guide - Data Classification Guide

External Standards: - NIST SP 800-171 . Safeguarding CUI - FERPA Overview . Student Privacy Law - EAR . Export Controls - HIPAA . Health Information Privacy

Tools: - gocryptfs Documentation - rclone . Cloud storage tool

Challenge

Challenge 12.1: De-identification Assessment

You have health survey data (not HIPAA-covered, but sensitive):

name,email,phone,birth_date,zip_code,medical_condition,medication
Jane Smith,j.smith@email.com,555-0123,1985-04-15,91010,Type 2 Diabetes,Metformin
John Doe,j.doe@email.com,555-0124,1990-07-22,91010,Hypertension,Lisinopril
Jane Loo,j.loo@email.com,555-0125,1988-11-30,91010,Type 2 Diabetes,Metformin

Tasks: 1. Identify all re-identification risks (direct, quasi, sensitive) 2. Propose de-identification strategy 3. Create de-identified version 4. Assess residual re-identification risk

Show me the solution

Risks: Names (direct), emails (direct), phone (direct), birth_date (quasi), zip_code (quasi, only 3 records for 91010), medical_condition (sensitive), medication (sensitive)

De-identification: - Remove: names, emails, phones, exact birth dates - Generalize: birth_date → year only; zip_code → state - Aggregate: medical_condition + medication together

Result:

birth_year,state,condition_medication
1985,CA,Diabetes/Metformin
1990,CA,Hypertension/Lisinopril
1988,CA,Diabetes/Metformin

Residual risk: Low (no direct identifiers, quasi-identifiers generalized, aggregated condition/med)

Key Points

• De-identification removes identifiers while preserving research value
• Four techniques: removal, aggregation, generalization, perturbation
• Keep mapping files encrypted and separate from de-identified data
• Publish data with DOI for reproducibility and impact
• Data use agreements formalize access and protection obligations
• Proper data classification protects your research, your community, and Pomona College