Hypothesis Testing under Maximal Leakage Privacy Constraints

2017/01/24 by Jiachun Liao, Lalitha Sankar, Liao, Jiachun +5 · 3 citations
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Information Theory (cs.IT) #Privacy-Preserving Technologies in Data #Wireless Communication Security Techniques

paper · pdf · doi:10.48550/arxiv.1701.07099

openalex publication_date 2017/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The problem of publishing privacy-guaranteed data for hypothesis testing is studied using the maximal leakage (ML) as a metric for privacy and the type-II error exponent as the utility metric. The optimal mechanism (random mapping) that maximizes utility for a bounded leakage guarantee is determined for the entire leakage range for binary datasets. For non-binary datasets, approximations in the high privacy and high utility regimes are developed. The results show that, for any desired leakage level, maximizing utility forces the ML privacy mechanism to reveal partial to complete knowledge about a subset of the source alphabet. The results developed on maximizing a convex function over a polytope may also of an independent interest.

Cited by

• Balancing Timeliness and Privacy in Discrete-Time Updating Systems
• Bounds on Maximal Leakage over Bayesian Networks
• Compressive Privacy for a Linear Dynamical System

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