Differentially-Private Bayes Consistency

• URL: http://arxiv.org/abs/2212.04216v1
• Date: Thu, 8 Dec 2022 11:57:30 GMT
• Title: Differentially-Private Bayes Consistency
• Authors: Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer
• Abstract summary: We construct a Bayes consistent learning rule that satisfies differential privacy (DP) We prove that any VC class can be privately learned in a semi-supervised setting with a near-optimal sample complexity.
• Score: 70.92545332158217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We construct a universally Bayes consistent learning rule that satisfies differential privacy (DP). We first handle the setting of binary classification and then extend our rule to the more general setting of density estimation (with respect to the total variation metric). The existence of a universally consistent DP learner reveals a stark difference with the distribution-free PAC model. Indeed, in the latter DP learning is extremely limited: even one-dimensional linear classifiers are not privately learnable in this stringent model. Our result thus demonstrates that by allowing the learning rate to depend on the target distribution, one can circumvent the above-mentioned impossibility result and in fact, learn \emph{arbitrary} distributions by a single DP algorithm. As an application, we prove that any VC class can be privately learned in a semi-supervised setting with a near-optimal \emph{labeled} sample complexity of $\tilde{O}(d/\varepsilon)$ labeled examples (and with an unlabeled sample complexity that can depend on the target distribution).

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