Cryptology ePrint Archive: Report 2017/1142

PIR with compressed queries and amortized query processing

Sebastian Angel and Hao Chen and Kim Laine and Srinath Setty

Abstract: Private information retrieval (PIR) is a key building block in many privacy-preserving systems. Unfortunately, existing constructions remain very expensive. This paper introduces two techniques that make the computational variant of PIR (CPIR) more efficient in practice. The first technique targets a recent class of CPU-efficient CPIR protocols where the query sent by the client contains a number of ciphertexts proportional to the size of the database. We show how to compresses this query, achieving size reductions of up to 274X.

The second technique is a new data encoding called probabilistic batch codes (PBCs). We use PBCs to build a multi-query PIR scheme that allows the server to amortize its computational cost when processing a batch of requests from the same client. This technique achieves up to 40× speedup over processing queries one at a time, and is significantly more efficient than related encodings. We apply our techniques to the Pung private communication system, which relies on a custom multi-query CPIR protocol for its privacy guarantees. By porting our techniques to Pung, we find that we can simultaneously reduce network costs by 36× and increase throughput by 3X.

Category / Keywords: PIR, private information retrieval, batch codes

Original Publication (with minor differences): IEEE Security and Privacy 2018 (Oakland)

Date: received 25 Nov 2017, last revised 1 Apr 2018

Contact author: sebs at cs utexas edu

Available format(s): PDF | BibTeX Citation

Note: This is the final version of our paper as it appears in S&P 2018.

Version: 20180401:225955 (All versions of this report)

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