PLASMA: Private, Lightweight Aggregated Statistics against Malicious Adversaries

Abstract

Private heavy-hitters is a data-collection task where multiple clients possess private bit strings, and data-collection servers aim to identify the most popular strings without learning anything about the clients' inputs. In this work, we introduce PLASMA: a private analytics framework in the three-server setting that protects the privacy of honest clients and the correctness of the protocol against a coalition of malicious clients and a malicious server. Our core primitives are a verifiable incremental distributed point function (VIDPF) and a batched consistency check, which are of independent interest. Our VIDPF introduces new methods to validate client inputs based on hashing. Meanwhile, our batched consistency check uses Merkle trees to validate multiple client sessions together in a batch. This drastically reduces server communication across multiple client sessions, resulting in significantly less communication compared to related works. Finally, we compare PLASMA with the recent works of Asharov et al. (CCS'22) and Poplar (S&P'21) and compare in terms of monetary cost for different input sizes.