Context-Aware Local Differential Privacy

Jul 12, 2020

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Local differential privacy (LDP) is a strong notion of privacy that often leads to a significant drop in utility. The original definition of LDP assumes that all the elements in the data domain are equally sensitive. However, in many real-life applications, some elements are more sensitive than others. We propose a context-aware framework for LDP that allows the privacy level to vary across the data domain, enabling system designers to place privacy constraints where they matter without paying the cost where they do not. For binary data domains, we provide a universally optimal privatization scheme and highlight its connections to Warner’s randomized response and Mangat’s improved response. Motivated by geo-location and web search applications, for k-ary data domains, we consider two special cases of context-aware LDP: block-structured LDP and high-low LDP. We study minimax discrete distribution estimation under both cases and provide communication-efficient, sample-optimal schemes, and information-theoretic lower bounds. We show, using worst-case analyses and experiments on Gowalla’s 3.6 million check-ins to 43,750 locations, that context-aware LDP achieves a far better accuracy under the same number of samples.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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