Welcome to the resource topic for 2023/080
Title:
PLASMA: Private, Lightweight Aggregated Statistics against Malicious Adversaries with Full Security
Authors: Dimitris Mouris, Pratik Sarkar, Nektarios Georgios Tsoutsos
Abstract:The private heavy-hitters problem is a data-collection task where many clients possess private bit strings, and data-collection servers aim to identify the most popular strings without learning anything about the clients’ inputs. The recent work of Poplar constructed a protocol for private heavy hitters but their solution was susceptible to additive attacks by a malicious server, compromising both the correctness and the security of the protocol.
In this paper, we introduce PLASMA, a private analytics framework that addresses these challenges by using three data-collection servers and a novel primitive, called verifiable incremental distributed point function (VIDPF). PLASMA allows each client to non-interactively send a message to the servers as its input and then go offline. Our new VIDPF primitive employs lightweight techniques based on efficient hashing and allows the servers to non-interactively validate client inputs and preemptively reject malformed ones.
PLASMA drastically reduces the communication overhead incurred by the servers using our novel batched consistency checks. Specifically, our server-to-server communication depends only on the number of malicious clients, as opposed to the total number of clients, yielding a 182\times and 235\times improvement over Poplar and other state-of-the-art sorting-based protocols respectively. Compared to recent works, PLASMA enables both client input validation and succinct communication, while ensuring full security. At runtime, PLASMA computes the 1000 most popular strings among a set of 1 million client-held 32-bit strings in 67 seconds and 256-bit strings in less than 20 minutes respectively.
ePrint: https://eprint.iacr.org/2023/080
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