Abstract: The present embodiments focus on the privacy-utility tradeoff encountered by a user who wishes to release some public data (denoted by X) to an analyst, that is correlated with his private data (denoted by S), in the hope of getting some utility. When noise is added as a privacy preserving mechanism, that is, Y=X+N, where Y is the actual released data to the analyst and N is noise, we show that adding Gaussian noise is optimal under 1_2-norm distortion for continuous data X. We denote the mechanism of adding Gaussian noise that minimizes the worst-case information leakage by Gaussian mechanism. The parameters for Gaussian mechanism are determined based on the eigenvectors and eigenvalues of the covariance of X. We also develop a probabilistic privacy preserving mapping mechanism for discrete data X, wherein the random discrete noise follows a maximum-entropy distribution.

Abstract: The present embodiments focus on the privacy-utility tradeoff encountered by a user who wishes to release some public data to an analyst, which is correlated with his private data, in the hope of getting some utility. When multiple data are released to one or more analyst, we design privacy preserving mappings in a decentralized fashion. In particular, each privacy preserving mapping is designed to protect against the inference of private data from each of the released data separately. Decentralization simplifies the design, by breaking one large joint optimization problem with many variables into several smaller optimizations with fewer variables.

Abstract: The present principles focus on the privacy-utility tradeoff encountered by a user who wishes to release some public data (denoted by X) to an analyst, that is correlated with his private data (denoted by S), in the hope of getting some utility. The public data is distorted before its release according to a probabilistic privacy preserving mapping mechanism, which limits information leakage under utility constraints. In particular, this probabilistic privacy mechanism is modeled as a conditional distribution, P_(Y|X), where Y is the actual released data to the analyst. The present principles design utility-aware privacy preserving mapping mechanisms against inference attacks, when only partial, or no, statistical knowledge of the prior distribution, P_(S,X), is available. Specifically, using maximal correlation techniques, the present principles provide a separability result on the information leakage that leads to the design of the privacy preserving mapping.