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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
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.
INDEX TERMS
private-information-retrieval, batch-codes, PIR, FHE, multi-query-PIR
CITATION

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.
doi:10.1109/SP.2018.00062
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