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Information-Theoretic Single-Server PIR in the Shuffle Model

Authors Yuval Ishai, Mahimna Kelkar, Daniel Lee, Yiping Ma

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Author Details

Yuval Ishai
  • Technion, Haifa, Israel
Mahimna Kelkar
  • Cornell University, New York, NY, USA
Daniel Lee
  • MIT, Cambridge, MA, USA
Yiping Ma
  • University of Pennsylvania, Philadelphia, PA, USA

Funding

This research was supported by a Google faculty grant. Yuval Ishai was additionally supported by ERC Project NTSC (742754), BSF grants 2018393 and 2022370, ISF grant 2774/20, and ISF-NSFC grant 3127/23. Mahimna Kelkar and Yiping Ma were partially supported by a Technion research scholarship. Yiping Ma was also supported by a Microsoft Research PhD Fellowship.

Cite AsGet BibTex

Yuval Ishai, Mahimna Kelkar, Daniel Lee, and Yiping Ma. Information-Theoretic Single-Server PIR in the Shuffle Model. In 5th Conference on Information-Theoretic Cryptography (ITC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 304, pp. 6:1-6:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.ITC.2024.6

Abstract

We revisit the problem of private information retrieval (PIR) in the shuffle model, where queries can be made anonymously by multiple clients. We present the first single-server PIR protocol in this model that has sublinear per-client communication and information-theoretic security. Moreover, following one-time preprocessing on the server side, our protocol only requires sublinear per-client computation. Concretely, for every γ > 0, the protocol has O(n^{γ}) communication and computation costs per (stateless) client, with 1/poly(n) statistical security, assuming that a size-n database is simultaneously accessed by poly(n) clients. This should be contrasted with the recent breakthrough result of Lin, Mook, and Wichs (STOC 2023) on doubly efficient PIR in the standard model, which is (inherently) limited to computational security.

Subject Classification

ACM Subject Classification
  • Security and privacy → Information-theoretic techniques
Keywords
  • Private information retrieval
  • Shuffle model

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