DP-Auditorium: A flexible library for auditing differential privacy

Differential privacy (DP) is a property of randomized mechanisms that limit the influence of any individual user’s information while processing and analyzing data. DP offers a robust solution to address growing concerns about data protection, enabling technologies across industries and government applications (e.g., the US census) without compromising individual user identities. As its adoption increases, it’s important to identify the potential risks of developing mechanisms with faulty implementations. Researchers have recently found errors in the mathematical proofs of private mechanisms, and their implementations. For example, researchers compared six sparse vector technique (SVT) variations and found that only two of the six actually met the asserted privacy guarantee. Even when mathematical proofs are correct, the code implementing the mechanism is vulnerable to human error.

However, practical and efficient DP auditing is challenging primarily due to the inherent randomness of the mechanisms and the probabilistic nature of the tested guarantees. In addition, a range of guarantee types exist, (e.g., pure DP, approximate DP, Rényi DP, and concentrated DP), and this diversity contributes to the complexity of formulating the auditing problem. Further, debugging mathematical proofs and code bases is an intractable task given the volume of proposed mechanisms. While ad hoc testing techniques exist under specific assumptions of mechanisms, few efforts have been made to develop an extensible tool for testing DP mechanisms.

To that end, in “DP-Auditorium: A Large Scale Library for Auditing Differential Privacy”, we introduce an open source library for auditing DP guarantees with only black-box access to a mechanism (i.e., without any knowledge of the mechanism’s internal properties)….