SI-19(6)

Requirement

Prevent disclosure of personally identifiable information by adding non-deterministic noise to the results of mathematical operations before the results are reported.

Discussion

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.

NIST 800-53A Assessment Guidance

Examine

[SELECT FROM: System and information integrity policy; system and information integrity procedures; personally identifiable information processing policy; de-identification procedures; system configuration; de-identified datasets; differential privacy tools; system security plan; privacy plan; privacy impact assessment; privacy risk assessment documentation; other relevant documents or records].

Interview

[SELECT FROM: Organizational personnel responsible for de-identifying the dataset; organizational personnel with information security and privacy responsibilities].

Test

[SELECT FROM: Online query systems; automated mechanisms supporting and/or implementing differential privacy].

ID	Assessment Objectives
SI-19(06)	the disclosure of personally identifiable information is prevented by adding non-deterministic noise to the results of mathematical operations before the results are reported.

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