Paper 2023/1552 Original: eprint.iacr.org/2023/1552 ↗

Doubly Efficient Batched Private Information Retrieval

Xiuquan Ding
Giulio Malavolta
Tianwei Zhang
Abstract

Private information retrieval (PIR) allows a client to read data from a server, without revealing which information they are interested in. A PIR is doubly efficient if the server runtime is, after a one-time pre-processing, sublinear in the database size. A recent breakthrough result from Lin, Mook, and Wichs [STOC’23] proposed the first-doubly efficient PIR with (online) server computation poly-logarithmic in the size of the database, assuming the hardness of the standard Ring-LWE problem. In this work, we consider the problem of doubly efficient batched PIR (DEBPIR), where the client wishes to download multiple entries. This problem arises naturally in many practical applications of PIR, or when the database contains large entries. Our main result is a construction of DEBPIR where the amortized communication and server computation overhead is $\tilde{O}(1)$, from the Ring-LWE problem. This represents an exponential improvement compared with known constructions, and it is optimal up to poly-logarithmic factors in the security parameter. Interestingly, the server’s online operations are entirely combinatorial and all algebraic computations are done in the pre-processing or delegated to the client.

Metadata
Available format(s)
PDF
Category
Cryptographic protocols
Publication info
Preprint.
History
2024-11-19: last updated
2023-10-09: published
Short URL
https://ia.cr/2023/1552
License
https://creativecommons.org/licenses/by/4.0/

BibTeX

@misc{cryptoeprint:2023/1552,
      author = {Xiuquan Ding and Giulio Malavolta and Tianwei Zhang},
      title = {Doubly Efficient Batched Private Information Retrieval},
      howpublished = {Cryptology {ePrint} Archive, Paper 2023/1552},
      year = {2023},
      url = {https://eprint.iacr.org/2023/1552}
}
Note: In order to protect the privacy of readers, this archive does not use cookies or embedded third party content.