SI-19(6)_differential-privacy

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

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

CWE Categories

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