NDSS 2022 Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

SESSION 2C-3 Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local while only exchanging model updates. Alas, this is not necessarily free from privacy and robustness vulnerabilities, e.g., via membership, property, and backdoor attacks. This paper investigates whether and to what extent one can use differential Privacy (DP) to protect both privacy and robustness in FL. To this end, we present a first-of-its-kind evaluation of Local and Central Differential Privacy (LDP/CDP) techniques in FL, assessing their feasibility and effectiveness.Our experiments show that both DP variants do defend against backdoor attacks, albeit with varying levels of protection-utility trade-offs, but anyway more effectively than other robustness defenses. DP also mitigates white-box membership inference attacks in FL, and our work is the first to show it empirically. Neither LDP nor CDP, however, defend against property inference. Overall, our work provides a comprehensive, re-usable measurement methodology to quantify the trade-offs between robustness/privacy and utility in differentially private FL.

PAPER

SLIDES

AUTHORS
Mohammad Naseri (University College London), Jamie Hayes (DeepMind), Emiliano De Cristofaro (University College London & Alan Turing Institute)

Network and Distributed System Security (NDSS) Symposium 2022, 24 – 28 April, 2022 Catamaran Resort Hotel & Spa, San Diego, California, and online.

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The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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