Welcome to the resource topic for 2023/1733
Hintless Single-Server Private Information Retrieval
Authors: Baiyu Li, Daniele Micciancio, Mariana Raykova, Mark Schultz-WuAbstract:
We present two new constructions for private information retrieval (PIR) in the classical setting where the clients do not need to do any preprocessing or store any database dependent information, and the server does not need to store any client-dependent information.
Our first construction HintlessPIR eliminates the client preprocessing step from the recent LWE-based SimplePIR (Henzinger et. al., USENIX Security 2023) by outsourcing the “hint” related computation to the server, leveraging a new concept of homomorphic encryption with composable preprocessing.
We realize this concept on RLWE encryption schemes, and thanks to the composibility of this technique we are able to preprocess almost all the expensive parts of the homomorphic computation and reuse across multiple executions.
As a concrete application, we achieve very efficient matrix vector multiplication that allows us to build HintlessPIR. For a database of size 8GB, HintlessPIR achieves throughput about 3.7GB/s without requiring any client or server state.
We additionally formalize the matrix vector multiplication protocol as LinPIR primitive, which may be of independent interests.
In our second construction TensorPIR we reduce the communications of HintlessPIR from square root to cubic root in the database size.
For this purpose we extend our HE with preprocessing techniques to composition of key-switching keys and the query expansion algorithm.
We show how to use RLWE encryption with preprocessing to outsource LWE decryption for ciphertexts generated by homomorphic multiplications.
This allows the server to do more complex processing using a more compact query under LWE.
We implement and benchmark HintlessPIR which achieves better concrete costs than TensorPIR for a large set of databases of interest.
We show that it improves the communication of recent preprocessing constructions when clients do not have large numbers of queries or database updates frequently.
The computation cost for removing the hint is small and decreases as the database becomes larger, and it is always more efficient than other constructions with client hints such as Spiral PIR (Menon and Wu, S&P 2022).
In the setting of anonymous queries we also improve on Spiral’s communication.
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