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XSPIR: Efficient Symmetrically Private Information Retrieval from Ring-LWE

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Computer Security – ESORICS 2022 (ESORICS 2022)

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Abstract

Private Information Retrieval (PIR) allows a client to retrieve one entry from a database held by a server, while hiding from the server which entry has been retrieved. Symmetrically Private Information Retrieval (SPIR) additionally protects the privacy of the data, requiring that the client obtains only its desired entry, and no information on other data entries.

In recent years, considerable effort has been expanded towards making PIR practical, reducing communication and computation. State-of-the-art PIR protocols are based on homomorphic encryption from the ring-LWE assumption. However, these efficient PIR protocols do not achieve database privacy, and leak a lot of information about other data entries, even when the client is honest. Generic transformation of these PIR protocols to SPIR have been suggested, but not implemented.

In this paper, we propose XSPIR, a practically efficient SPIR scheme. Our scheme is based on homomorphic encryption from ring-LWE like recent PIR works, but achieves a stronger security guarantee with low performance overhead. We implement XSPIR, and run experiments comparing its performance against SealPIR (Angel et al., IEEE S &P 2018) and MulPIR (Ali et al., USENIX SECURITY 2021). We find that, even though our scheme achieves a stronger security guarantee, our performance is comparable to these state-of-the-art PIR protocols.

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Notes

  1. 1.

    We leave the size of each element implicit as it does not affect the definition.

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Acknowledgement

This research was supported in part by the U.S. Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research under award number DE-SC-0001234, a grant from the Columbia-IBM center for Blockchain and Data Transparency, by LexisNexis risk solutions, and by JPMorgan Chase & Co. Any views or opinions expressed herein are solely those of the authors listed.

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  1. Columbia University, New York, NY, 10027, USA

    Chengyu Lin, Zeyu Liu & Tal Malkin

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  1. Chengyu Lin
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Correspondence to Chengyu Lin .

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  1. Rutgers University, Newark, NJ, USA

    Vijayalakshmi Atluri

  2. Hamad Bin Khalifa University, Doha, Qatar

    Roberto Di Pietro

  3. Technical University of Denmark, Kongens Lyngby, Denmark

    Christian D. Jensen

  4. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

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Lin, C., Liu, Z., Malkin, T. (2022). XSPIR: Efficient Symmetrically Private Information Retrieval from Ring-LWE. In: Atluri, V., Di Pietro, R., Jensen, C.D., Meng, W. (eds) Computer Security – ESORICS 2022. ESORICS 2022. Lecture Notes in Computer Science, vol 13554. Springer, Cham. https://doi.org/10.1007/978-3-031-17140-6_11

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