2018/01/05 by Borzoo Rassouli, Rassouli, Borzoo, Denız Gündüz +1 · 3 citations
Computer Science · #Adversarial Robustness in Machine Learning #Cryptography and Data Security #FOS: Computer and information sciences #Information Theory (cs.IT) #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.1801.02505
openalex publication_date 2018/01/05 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
The total variation distance is proposed as a privacy measure in an information disclosure scenario when the goal is to reveal some information about available data in return of utility, while retaining the privacy of certain sensitive latent variables from the legitimate receiver. The total variation distance is introduced as a measure of privacy-leakage by showing that: i) it satisfies the post-processing and linkage inequalities, which makes it consistent with an intuitive notion of a privacy measure; ii) the optimal utility-privacy trade-off can be solved through a standard linear program when total variation distance is employed as the privacy measure; iii) it provides a bound on the privacy-leakage measured by mutual information, maximal leakage, or the improvement in an inference attack with a bounded cost function.