differential-privacy-library

differential-privacy-library is included in the Awesome MLSecOps Privacy-Preserving Machine Learning directory. The community-maintained README describes it as: “Library designed for differential privacy and machine learning.” Its MLSecOps relevance is the reduction or measurement of sensitive-data exposure through differential privacy, anonymization, encrypted computation, or privacy attack testing. The linked first-party source is the IBM/differential-privacy-library repository on GitHub. A technical review should test the project's documented evidence across four criteria: Explicit privacy guarantees, Utility and accuracy impact, Re-identification testing, and Repeatable privacy accounting. Compare that evidence with the intended architecture and threat model. Catalog inclusion establishes relevance to this security category; it is not a certification, comparative ranking, or endorsement. Confirm current capabilities, maintenance, licensing, limitations, and deployment assumptions in the first-party documentation before adoption.

Library designed for differential privacy and machine learning

Neutral catalog description synchronized from the Awesome MLSecOps README