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Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
May 2, 2024, 4:43 a.m. | Yingyu Lin, Yi-An Ma, Yu-Xiang Wang, Rachel Redberg, Zhiqi Bu
cs.LG updates on arXiv.org arxiv.org
Abstract: Posterior sampling, i.e., exponential mechanism to sample from the posterior distribution, provides $\varepsilon$-pure differential privacy (DP) guarantees and does not suffer from potentially unbounded privacy breach introduced by $(\varepsilon,\delta)$-approximate DP. In practice, however, one needs to apply approximate sampling methods such as Markov chain Monte Carlo (MCMC), thus re-introducing the unappealing $\delta$-approximation error into the privacy guarantees. To bridge this gap, we propose the Approximate SAample Perturbation (abbr. ASAP) algorithm which perturbs an MCMC sample …
abstract apply arxiv breach cs.lg delta differential differential privacy distribution however markov mcmc posterior practice privacy sample sampling stat.ml tractable type
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