+SI-19(06) Differential Privacy
|
SI-19(06) Differential Privacy
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.
Assessment Objective
the disclosure of personally identifiable information is prevented by adding non-deterministic noise to the results of mathematical operations before the results are reported.
Assessment Method
Examine
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
Organizational personnel responsible for de-identifying the dataset
organizational personnel with information security and privacy responsibilities
1. Pārskats
1.1 Atsauces
1.2 Identificētās prasības
1.3 Saistītie tiesību akti
2. Identificētās prasības
3. Saistītie tiesību akti
Tiesību akti
| Avots |
Regulējums |
Saistītie uzdevumi
|