Private Stateful Information Retrieval
Sarvar Patel, Giuseppe Persiano, Kevin Yeo
摘要
Private information retrieval (PIR) is a fundamental tool for preserving query privacy when accessing outsourced data. All previous PIR constructions have significant costs preventing widespread use. In this work, we present private stateful information retrieval (PSIR), an extension of PIR, allowing clients to be stateful and maintain information between multiple queries. Our design of the PSIR primitive maintains three important properties of PIR: multiple clients may simultaneously query without complex concurrency primitives, query privacy should be maintained if the server colludes with other clients, and new clients should be able to enroll into the system by exclusively interacting with the server. We present a PSIR framework that reduces an online query to performing one single-server PIR on a sub-linear number of database records. All other operations beyond the single-server PIR consist of cryptographic hashes or plaintext operations. In practice, the dominating costs of resources occur due to the public-key operations involved with PIR. By reducing the input database to PIR, we are able to limit expensive computation and avoid transmitting large ciphertexts. We show that various instantiations of PSIR reduce server CPU by up to 10x and online network costs by up to 10x over the previous best PIR construction.
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引用它的顶会 Paper17
- SPIRAL: Fast, High-Rate Single-Server PIR via FHE CompositionSamir Jordan Menon, David J. WuS&P 2022 · 被引用 153 次
- Communication-Computation Trade-offs in PIRAsra Ali, Tancrède Lepoint, Sarvar Patel, Mariana Raykova 等USENIX Security 2021 · 被引用 126 次
- Billion-scale federated learning on mobile clients: a submodel design with tunable privacyChaoyue Niu, Fan Wu, Shaojie Tang, Lifeng Hua 等MobiCom 2020 · 被引用 114 次
- Private Blocklist Lookups with ChecklistDmitry Kogan, Henry Corrigan-GibbsUSENIX Security 2021 · 被引用 104 次
- Single-Server Private Information Retrieval with Sublinear Amortized TimeHenry Corrigan-Gibbs, Alexandra Henzinger, Dmitry KoganEUROCRYPT 2022 · 被引用 64 次
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