2019/10/17 by Milan Lopuhaä-Zwakenberg, Lopuhaä-Zwakenberg, Milan, Boris Škorić +3 · 2 citations
Computer Science · Engineering · #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information Theory (cs.IT) #Privacy-Preserving Technologies in Data #Wireless Communication Security Techniques
paper · pdf · doi:10.48550/arxiv.1910.07826
openalex publication_date 2019/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Local Differential Privacy (LDP) protocols allow an aggregator to obtain population statistics about sensitive data of a userbase, while protecting the privacy of the individual users. To understand the tradeoff between aggregator utility and user privacy, we introduce new information-theoretic metrics for utility and privacy. Contrary to other LDP metrics, these metrics highlight the fact that the users and the aggregator are interested in fundamentally different domains of information. We show how our metrics relate to ε-LDP, the de facto standard privacy metric, giving an information-theoretic interpretation to the latter. Furthermore, we use our metrics to quantitatively study the privacy-utility tradeoff for a number of popular protocols.