CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated LearningDownload PDF

Published: 01 Feb 2023, 19:22, Last Modified: 01 Mar 2023, 17:10ICLR 2023 posterReaders: Everyone
Keywords: Federated Learning, Differential Privacy, Empirical Privacy, Model Auditing, Membership Inference Attack
TL;DR: Crafting canaries to measure empirical privacy of DP-FL training under a realistic threat model
Abstract: Federated Learning (FL) is a setting for training machine learning models in distributed environments where the clients do not share their raw data but instead send model updates to a server. However, model updates can be subject to attacks and leak private information. Differential Privacy (DP) is a leading mitigation strategy which involves adding noise to clipped model updates, trading off performance for strong theoretical privacy guarantees. Previous work has shown that the threat model of DP is conservative and that the obtained guarantees may be vacuous or may overestimate information leakage in practice. In this paper, we aim to achieve a tighter measurement of the model exposure by considering a realistic threat model. We propose a novel method, CANIFE, that uses canaries - carefully crafted samples by a strong adversary to evaluate the empirical privacy of a training round. We apply this attack to vision models trained on CIFAR-10 and CelebA and to language models trained on Sent140 and Shakespeare. In particular, in realistic FL scenarios, we demonstrate that the empirical per-round epsilon obtained with CANIFE is 4 -- 5$\times$ lower than the theoretical bound.
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