Enhancing utility and privacy with noisy minimax filters

Abstract

Preserving privacy of continuous and/or high-dimensional data such as images, videos and audios is challenging. Syntactic anonymization methods were proposed typically for discrete data types and can be unsuitable. Differential privacy, which provides a stricter type of privacy, has shown more success in sanitizing continuous data. However, both syntactic and differential privacy are susceptible to inference attacks, i.e., an adversary can accurately guess sensitive attributes from insensitive attributes. On the other hand, minimax filters were proposed previously to minimize the accuracy of inference while maximizing utility at the same time. The paper presents noisy minimax filter that combines minimax filter and differentially private mechanism, which can attain high average utility and protection against inference attacks and a formal worst-case privacy guarantee. The proposed algorithm is demonstrated with real databases of faces, voices, and motion data.