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Fundamental Trade-Offs between Privacy and Utility in Data Sharing (2/3)
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University of New South Wales

2021 Croucher Summer Course in Information Theory, The Chinese University of Hong Kong




A main barrier in sharing data between people and organizations is legitimate concerns about privacy. To address this concern, an active research area has focused on designing data perturbation mechanisms that can maintain usefulness of shared data for a given analytical task, while minimizing the capability of the data analyst in inferring sensitive information. However, there is often a fundamental and nontrivial trade-off between data privacy and utility. This lecture aims to provide a thorough understanding of this trade-off and optimal ways to deal with it. The primary focus will be on information theoretic aspects of this problem. We will explore questions such as how to meaningfully and operationally measure privacy leakage using mutual information and its various generalizations in the literature, such as Sibson mutual information and maximal leakage. We will formulate the problem of privacy-utility trade-off and explore theoretical bounds on optimal solutions, as well as novel privacy-preserving algorithms to achieve them. We will also present new connections between information-theoretic privacy and other privacy-preserving frameworks, most notably differential privacy, identifiability, and low-influence. The lecture aims to provide many illustrative and interactive examples to establish key concepts and discuss practical applications of data perturbation for discrete-valued queries on datasets (such as counting or voting) and statistical queries on datasets (such as mean or quantiles).

Parastoo Sadeghi received the bachelor and master degrees in electrical engineering from Sharif University of Technology, Tehran, Iran, in 1995 and 1997, respectively, and the Ph.D. degree in electrical engineering from the University of New South Wales (UNSW) Sydney, in 2006. She is currently a Professor at the School of Engineering and Information Technology, UNSW Canberra. Her research interests include information theory, data privacy, index coding, and network coding. She has co-authored the book Hilbert Space Methods in Signal Processing (Cambridge University Press, 2013) and around 170 refereed journal articles and conference papers. In 2019, she was awarded a Future Fellowship from the Australian Research Council. From 2016 to 2019, she served as an Associate Editor for the IEEE Transactions on Information Theory. She has also served on the Board of Governors of the IEEE Information Theory Society.