Ensuring Privacy with Constrained Additive Noise by Minimizing Fisher Information

TL;DR

This paper introduces a privacy-preserving method using constrained additive noise optimized via Fisher information minimization, providing a new approach to protect database entries against inference attacks.

Contribution

It develops a Fisher information-based privacy measure and derives the optimal noise distribution under constraints, extending the approach to dynamic scenarios and comparing with differential privacy.

Findings

Optimal noise distribution minimizes Fisher information trace

The method provides quantifiable privacy guarantees via Cramer-Rao bound

Extensions to dynamic privacy problems are demonstrated

Abstract

The problem of preserving the privacy of individual entries of a database when responding to linear or nonlinear queries with constrained additive noise is considered. For privacy protection, the response to the query is systematically corrupted with an additive random noise whose support is a subset or equal to a pre-defined constraint set. A measure of privacy using the inverse of the trace of the Fisher information matrix is developed. The Cramer-Rao bound relates the variance of any estimator of the database entries to the introduced privacy measure. The probability density that minimizes the trace of the Fisher information (as a proxy for maximizing the measure of privacy) is computed. An extension to dynamic problems is also presented. Finally, the results are compared to the differential privacy methodology.