Towards Doubly Efficient Private Information Retrieval

Canetti, Ran; Holmgren, Justin; Richelson, Silas

doi:10.1007/978-3-319-70503-3_23

Cited by 43 publications

(15 citation statements)

References 17 publications

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“…ORAM and its multiple client extensions do not provide practical solutions for enabling access to large groups of clients efficiently and privately compared to PSIR. [23,24,44] ✓ ✓ ✓ Multiple Client ORAM [34,38,39,50] ✓ ✓ ✓ ✓ Symmetric-Key DEPIR [12,15] ✓ Public-Key DEPIR [12] ✓ ✓ PANDA [28] ✓ ✓ ✓ Table 1: This table compares single-server privacy-preserving storage systems. Multiple client access refers to systems that enable accessibility to large groups of clients while providing privacy against subsets of colluding clients.…”

Section: Relation To Other Privacy-preserving Storage Primitivesmentioning

confidence: 99%

“…The first public-key DEPIR construction was presented by Beimel et al [9] in the multiple, non-colluding server setting that is information-theoretically secure. More recently, Boyle et al [12] and Canetti et al [15] present symmetric-key DEPIR schemes in the single-server setting using locally decodable codes built from Reed-Muller codes [42,46]. In addition, Boyle et al…”

Section: Doubly Efficient Pir Doubly Efficient Pir (Depir) Refers Tomentioning

confidence: 99%

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Private Stateful Information Retrieval

Patel

Persiano

Yeo

2018

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security

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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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Section: Relation To Other Privacy-preserving Storage Primitivesmentioning

confidence: 99%

Section: Doubly Efficient Pir Doubly Efficient Pir (Depir) Refers Tomentioning

confidence: 99%

Private Stateful Information Retrieval

Patel

Persiano

Yeo

2018

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security

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“…The second proposal described by Beimel et al [16] is to preprocess the database in certain IT-PIR schemes to reduce the cost of future queries. Since this works well, recent projects [19,26] employ an analogous approach in CPIR schemes. However, making the preprocessed database accessible by more than one client under these schemes requires cryptographic primitives that are currently too inefficient to be implemented (virtual black-box obfuscation [14] heuristically instantiated from indistinguishability obfuscation [43]).…”

Section: Existing Multi-query Pir Schemesmentioning

confidence: 99%

PIR with Compressed Queries and Amortized Query Processing

Angel

Chen

Laine

et al. 2018

2018 IEEE Symposium on Security and Privacy (SP)

142

129

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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 274×.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 3×.

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“…Hence we say a PIR protocol is non-trivial if it is communicationwise better than the trivial solution. In the literature, many efficient PIR protocols have been proposed, either in singleserver setting [2,3], or in multi-server setting [12,14,21]. As for the second question, similarly, we say a secure submodel aggregation protocol is non-trivial if it is concretely better than the full model secure aggregation protocol in terms of communication.…”

Section: Introductionmentioning

confidence: 99%

“…Existing solutions [5,23,26,37] leverages different tools, i.e. Differential Privacy (DP) [18,36], Multi-Party Computation (MPC) [15] or PIR [2,3,12,14,21]. However, some work [23,26] consider only the non-adaptive case, where each client selects a fixed submodel prior to the training.…”

Section: Introductionmentioning

confidence: 99%

Practical and Light-weight Secure Aggregation for Federated Submodel Learning

Cui¹,

Chen²,

Ye³

et al. 2021

Preprint

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Recently, Niu, et. al. [37] introduced a new variant of Federated Learning (FL), called Federated Submodel Learning (FSL). Different from traditional FL, each client locally trains the submodel (e.g., retrieved from the servers) based on its private data and uploads a submodel at its choice to the servers. Then all clients aggregate all their submodels and finish the iteration. Inevitably, FSL introduces two privacy-preserving computation tasks, i.e., Private Submodel Retrieval (PSR) and Secure Submodel Aggregation (SSA). Existing work fails to provide a loss-less scheme, or has impractical efficiency. In this work, we leverage Distributed Point Function (DPF) and cuckoo hashing to construct a practical and light-weight secure FSL scheme in the two-server setting. More specifically, we propose two basic protocols with few optimisation techniques, which ensures our protocol practicality on specific real-world FSL tasks. Our experiments show that our proposed protocols can finish in less than 1 minute when weight sizes ≤ 2 15 , we also demonstrate protocol efficiency by comparing with existing work [37] and by handling a real-world FSL task.

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Towards Doubly Efficient Private Information Retrieval

Cited by 43 publications

References 17 publications

Private Stateful Information Retrieval

Private Stateful Information Retrieval

PIR with Compressed Queries and Amortized Query Processing

Practical and Light-weight Secure Aggregation for Federated Submodel Learning

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