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Poster
Privacy Amplification via Random Check-Ins
Borja Balle · Peter Kairouz · Brendan McMahan · Om Dipakbhai Thakkar · Abhradeep Thakurta

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #647

Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fundamental building block in many applications for learning over sensitive data. Two standard approaches, privacy amplification by subsampling, and privacy amplification by shuffling, permit adding lower noise in DP-SGD than via na\"{\i}ve schemes. A key assumption in both these approaches is that the elements in the data set can be uniformly sampled, or be uniformly permuted --- constraints that may become prohibitive when the data is processed in a decentralized or distributed fashion. In this paper, we focus on conducting iterative methods like DP-SGD in the setting of federated learning (FL) wherein the data is distributed among many devices (clients). Our main contribution is the \emph{random check-in} distributed protocol, which crucially relies only on randomized participation decisions made locally and independently by each client. It has privacy/accuracy trade-offs similar to privacy amplification by subsampling/shuffling. However, our method does not require server-initiated communication, or even knowledge of the population size. To our knowledge, this is the first privacy amplification tailored for a distributed learning framework, and it may have broader applicability beyond FL. Along the way, we improve the privacy guarantees of amplification by shuffling and show that, in practical regimes, this improvement allows for similar privacy and utility using data from an order of magnitude fewer users.

Author Information

Borja Balle (DeepMind)
Peter Kairouz (Google)

Peter Kairouz is a Google Research Scientist working on decentralized, privacy-preserving, and robust machine learning algorithms. Prior to Google, his research largely focused on building decentralized technologies for anonymous broadcasting over complex networks, understanding the fundamental trade-off between differential privacy and utility of learning algorithms, and leveraging state-of-the-art deep generative models for data-driven privacy and fairness.

Brendan McMahan (Google)
Om Thakkar (Google)
Abhradeep Thakurta (Google)

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