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Optimal Local Bayesian Differential Privacy over Markov Chains. (arXiv:2206.11402v1 [cs.CR])
June 24, 2022, 1:20 a.m. | Darshan Chakrabarti, Jie Gao, Aditya Saraf, Grant Schoenebeck, Fang-Yi Yu
cs.CR updates on arXiv.org arxiv.org
In the literature of data privacy, differential privacy is the most popular
model. An algorithm is differentially private if its outputs with and without
any individual's data are indistinguishable. In this paper, we focus on data
generated from a Markov chain and argue that Bayesian differential privacy
(BDP) offers more meaningful guarantees in this context. Our main theoretical
contribution is providing a mechanism for achieving BDP when data is drawn from
a binary Markov chain. We improve on the state-of-the-art …
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