SI-19(6)_differential-privacy
DevOps & SecurityPrevent disclosure of personally identifiable information by adding non-deterministic noise to the results of mathematical operations before the resul
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SI-19(6) Differential Privacy
Enhancement of: SI-19
High-Level Description
Family: System and Information Integrity (SI) Framework: NIST SP 800-53 Rev 5
The mathematical definition for differential privacy holds that the result of a dataset analysis should be approximately the same before and after the addition or removal of a single data record (which is assumed to be the data from a single individual). In its most basic form, differential privacy applies only to online query systems. However, it can also be used to produce machine-learning statistical classifiers and synthetic data. Differential privacy comes at the cost of decreased accuracy of results, forcing organizations to quantify the trade-off between privacy protection and the overall accuracy, usefulness, and utility of the de-identified dataset. Non-deterministic noise can include adding small, random values to the results of mathematical operations in dataset analysis.
What to Check
- Verify SI-19(6) Differential Privacy is documented in SSP
- Confirm control is operating effectively
- Review evidence of continuous monitoring for SI-19(6)
- Verify enhancement builds upon base control SI-19
How to Test
Step 1: Review Documentation
Examine the System Security Plan (SSP) and related artifacts for SI-19(6) implementation details. Verify the organization has documented how this control is satisfied.
Step 2: Validate Implementation
# For cloud environments, use cloud-audit-mcp tools
# For on-premises, review system configurations directly
# Example: Check if account management policies exist
grep -r "account.management\|access.control" /etc/security/ 2>/dev/null
Step 3: Test Operating Effectiveness
Verify the control is actively functioning, not just documented. Check logs, configurations, and operational evidence.
Tools
| Tool | Purpose | Usage |
|---|---|---|
| cloud-audit-mcp | Check integrity monitoring | cloud_audit_monitoring |
| AWS CLI | Review GuardDuty/Inspector | aws guardduty list-detectors |
Remediation Guide
Control Statement
Prevent disclosure of personally identifiable information by adding non-deterministic noise to the results of mathematical operations before the results are reported.
Implementation Guidance
The mathematical definition for differential privacy holds that the result of a dataset analysis should be approximately the same before and after the addition or removal of a single data record (which is assumed to be the data from a single individual). In its most basic form, differential privacy applies only to online query systems. However, it can also be used to produce machine-learning statistical classifiers and synthetic data. Differential privacy comes at the cost of decreased accuracy of results, forcing organizations to quantify the trade-off between privacy protection and the overall accuracy, usefulness, and utility of the de-identified dataset. Non-deterministic noise can include adding small, random values to the results of mathematical operations in dataset analysis.
Risk Assessment
| Finding | Severity | Impact |
|---|---|---|
| SI-19(6) Differential Privacy not implemented | High | System and Information Integrity |
| SI-19(6) partially implemented | Medium | Incomplete System and Information Integrity |
CWE Categories
| CWE ID | Title |
|---|---|
| CWE-20 | Improper Input Validation |
References
- NIST SP 800-53 Rev 5 - SI-19(6)
- NIST SP 800-53A Rev 5 (Assessment Procedures)
- NIST SP 800-53 Rev 5 Full Catalog
Checklist
- Control documented in SSP
- Implementation evidence collected
- Operating effectiveness validated
- Continuous monitoring in place
- Related controls (SC-12, SC-13) reviewed