CPP: Towards comprehensive privacy preserving for query processing in information networks

Chaobin Liu, Shuigeng Zhou, Haibo Hu, Yuzhe Richard Tang, Jihong Guan, Yao Ma

Abstract: Highlights • A novel framework is proposed to provide comprehensive privacy preserving for query processing in information networks, including data privacy, storage privacy and query privacy. • A basic implementation scheme for supporting key-value search under this framework is devised, where search key and commutative encryption are carefully designed for guaranteeing the security of the framework. • A more efficient implementation scheme is developed based on a tradeoff mechanism between security and efficiency, for efficient query processing over large amounts of data. • Both theoretical analysis and experimental results are given, which validate the proposed framework and the implementation schemes. Abstract This paper addresses the privacy issue for query processing in information networks, where users search and aggregate information from many data sources. To cater for the privacy requirements of all parties, we propose a comprehensive privacy preserving framework (CPP in short) for preserving data privacy, query privacy and storage privacy simultaneously, and implement this framework for key-value query processing. We first develop a baseline scheme that adopts commutative encryption with full indistinguishability guarantee. To speedup query processing, we then propose a tradeoff between security and efficiency, which leads to a scheme that significantly reduces the use of commutative encryption with a little and bounded security cost. Finally, we validate the proposed framework and the implementation schemes by both theoretical analysis and experimental results.