2023/04/16 by Sara Saeidian, Saeidian, Sara, Giulia Cervia +5 · 4 citations
Computer Science · #Adversarial Robustness in Machine Learning #Cryptographic Implementations and Security #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information Theory (cs.IT)
paper · pdf · doi:10.48550/arxiv.2304.07722
openalex publication_date 2023/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Pointwise maximal leakage (PML) is an operationally meaningful privacy measure that quantifies the amount of information leaking about a secret X to a single outcome of a related random variable Y. In this paper, we extend the notion of PML to random variables on arbitrary probability spaces. We develop two new definitions: First, we extend PML to countably infinite random variables by considering adversaries who aim to guess the value of discrete (finite or countably infinite) functions of X. Then, we consider adversaries who construct estimates of X that maximize the expected value of their corresponding gain functions. We use this latter setup to introduce a highly versatile form of PML that captures many scenarios of practical interest whose definition requires no assumptions about the underlying probability spaces.