The Importance of DP in AI Mechanisms
Differential Privacy (DP) is crucial to secure AI systems in processing and analyzing data without compromising users’ identities. However, faulty implementations are a significant concern in developing DP mechanisms. Recently, researchers have found errors and need a practical and efficient way to audit such mechanisms.
Introducing DP-Auditorium
DP-Auditorium is a Python-based open source library for auditing DP mechanisms. It uses black-box optimization to identify privacy guarantee violations and suggest datasets where the privacy guarantee may fail.
DP Guarantees and DP-Auditorium Features
DP mechanisms have specific privacy guarantees, such as pure DP, approximate DP, Rényi DP, and concentrated DP. DP-Auditorium has new testing algorithms that perform divergence optimization for these privacy guarantees. It also introduces three property testers and dataset finders to detect privacy violations efficiently.
Key Results
Results show that DP-Auditorium is effective in identifying privacy bugs in both private and non-private mechanisms. For example, it found faulty implementations of Laplace, Gaussian, and DP gradient descent algorithms that could compromise data privacy.
Overall, DP-Auditorium is a promising tool for testing DP mechanisms and ensuring data privacy in AI systems.