Welcome to the resource topic for 2023/1641
PSKPIR: Symmetric Keyword Private Information Retrieval based on PSI with Payload
Authors: Zuodong Wu, Dawei Zhang, Yong Li, Xu HanAbstract:
Symmetric Private Information Retrieval (SPIR) is a protocol that protects privacy during data transmission. However, the existing SPIR focuses only on the privacy of the data to be requested on the server, without considering practical factors such as the payload that may be present during data transmission. This could seriously prevent SPIR from being applied to many complex data scenarios and hinder its further expansion. To solve such problems, we propose a primitive (PSKPIR) for symmetric private keyword information retrieval based on private set intersection (PSI) that supports payload transmission and batch keyword search. Specifically, we combine probe-and-XOR of strings (PaXoS) and Oblivious Programmable PRF (OPPRF) to construct PSI with payload (PSI-Payload) not only satisfies client privacy and server privacy, but also facilitates efficient payload transmission. The client can efficiently generate symmetric keys locally using keywords in the intersection, and receive payloads with matching labels in batches. In addition, we provide security definitions for PSKPIR and use the framework of universal composability (UC) to prove security. Finally, we implement PSKPIR with sublinear communication costs in both LAN and WAN settings. Experimental results show that our payload transfer speed is 10× faster than previous work on sufficiently large data sets.
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