SI-19(6) Differential Privacy
Control enhancement of SI-19 De-identification
Control
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.
Related controls
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Control catalog and framework text on this page is reproduced from official NIST publications — a public-domain work of the U.S. Government (17 U.S.C. §105). Maverc commentary, groupings, and readiness guidance are our own and are not part of any NIST publication.