Computationally Secure Aggregation and Private Information Retrieval in the Shuffle Model

Adrià Gascón, Yuval Ishai, Mahimna Kelkar, Baiyu Li, Yiping Ma, Mariana Raykova

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The shuffle model has recently emerged as a popular setting for differential privacy, where clients can communicate with a central server using anonymous channels or an intermediate message shuffler. This model was also explored in the context of cryptographic tasks such as secure aggregation and private information retrieval (PIR). However, this study was almost entirely restricted to the stringent notion of information-theoretic security. In this work, we study computationally secure aggregation protocols and PIR in the shuffle model. Our starting point is the insight that the previous technique of shuffling additive shares can be improved in the computational setting. We show that this indeed holds under the standard learning parity with noise (LPN) assumption, but even better efficiency follows from plausible conjectures about the multi-disjoint syndrome decoding (MDSD) problem that we introduce and study in this work. We leverage the above towards improving the efficiency of secure aggregation and PIR in the shuffle model. For secure aggregation of long vectors, our protocols require 9×–25× less communication than the previous information-theoretic solutions. Our PIR protocols enjoy the simplicity and concrete efficiency benefits of multi-server PIR while only requiring a single server to store the database. Under the MDSD assumption, they improve over recent single-server PIR constructions by up to two orders of magnitude.

Original languageEnglish
Title of host publicationCCS 2024 - Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security
Pages4122-4136
Number of pages15
ISBN (Electronic)9798400706363
DOIs
StatePublished - 9 Dec 2024
Event31st ACM SIGSAC Conference on Computer and Communications Security, CCS 2024 - Salt Lake City, United States
Duration: 14 Oct 202418 Oct 2024

Publication series

NameCCS 2024 - Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security

Conference

Conference31st ACM SIGSAC Conference on Computer and Communications Security, CCS 2024
Country/TerritoryUnited States
CitySalt Lake City
Period14/10/2418/10/24

Keywords

  • private information retrieval
  • secure aggregation
  • shuffle model
  • sparse LPN
  • syndrome decoding
  • variable-size records

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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