The Communication Cost of Security and Privacy in Federated Frequency Estimation

International Conference on Artificial Intelligence and Statistics (AISTATS)


We consider the federated frequency estimation problem, where each user holds a private item Xi from a size-d domain and a server aims to estimate the empirical frequency (i.e., histogram) of n items with n ≪ d. Without any security and privacy considerations, each user can communicate their item to the server by using log d bits. A naive application of secure aggregation protocols would, however, require d log n bits per user. Can we reduce the communication needed for secure aggregation, and does security come with a fundamental cost in communication?

In this paper, we develop an information theoretic model for secure aggregation that allows us to characterize the fundamental cost of security and privacy in terms of communication. We show that with security (and without privacy) Ω (n log d) bits per user are necessary and sufficient to allow the server to compute the frequency distribution. This is significantly smaller than the d log n bits per user needed by the naive scheme, but significantly higher than the log d bits per user needed without security. To achieve differential privacy, we construct a linear scheme based on a noisy sketch that locally perturbs the data and does not require a trusted server (a.k.a. distributed differential privacy). We analyze this scheme under ℓ2 and ℓ∞ loss. By using our information-theoretic framework, we show that the scheme achieves the optimal accuracy-privacy trade-off with optimal communication cost, while matching the performance in the centralized case where data is stored in the central server.

Featured Publications