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Paper Detail

Paper IDP.4.5
Paper Title Privacy-Utility Tradeoff in a Guessing Framework Inspired by Index Coding
Authors Yucheng Liu, Australian National University, Australia; Ni Ding, Commonwealth Scientific and Industrial Research Organisation, Australia; Parastoo Sadeghi, Australian National University, Australia; Thierry Rakotoarivelo, Commonwealth Scientific and Industrial Research Organisation, Australia
Session P.4: Information Privacy II
Presentation Lecture
Track Cryptography, Security and Privacy
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
Abstract This paper studies the tradeoff in privacy and utility in a single-trial multi-terminal guessing (estimation) framework using a system model that is inspired by index coding. There are n independent discrete sources at a data curator. There are m legitimate users and one adversary, each with some side information about the sources. The data curator broadcasts a distorted function of sources to legitimate users, which is also overheard by the adversary. In terms of utility, each legitimate user wishes to perfectly reconstruct some of the unknown sources and attain a certain gain in the estimation correctness for the remaining unknown sources. In terms of privacy, the data curator wishes to minimize the maximal leakage: the worst-case guessing gain of the adversary in estimating any target function of its unknown sources after receiving the broadcast data. Given the system settings, we derive fundamental performance lower bounds on the maximal leakage to the adversary, which are inspired by the notion of confusion graph and performance bounds for the index coding problem. We also detail a greedy privacy enhancing mechanism, which is inspired by the agglomerative clustering algorithms in the information bottleneck and privacy funnel problems.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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