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2018 IEEE Symposium on Security and Privacy (SP) (2018)
San Francisco, CA, US
May 21, 2018 to May 23, 2018
ISSN: 2375-1207
ISBN: 978-1-5386-4353-2
pp: 1011-1028
Sebastian Angel , The University of Texas at Austin and New York University
Hao Chen , Microsoft Research
Kim Laine , Microsoft Research
Srinath Setty , Microsoft Research
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.
private-information-retrieval, batch-codes, PIR, FHE, multi-query-PIR

S. Angel, H. Chen, K. Laine and S. Setty, "PIR with compressed queries and amortized query processing," 2018 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, US, , pp. 1011-1028.
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