Achieving personalized and privacy-preserving range queries over outsourced cloud data
Yao Shen, Liusheng Huang, Wei Yang · 2017
With the increasing prevalence of cloud computing, data owners prefer to outsource their databases to the cloud. For the protection of data privacy, sensitive data have to be encrypted before outsourcing, which introduces much difficulty into effective data utilization. Most previous studies either suffer from privacy disclosure and low efficiency, or do not support personalized multidimensional range queries. In this paper, we focus on personalized private range queries over outsourced data. We propose a personalized and privacy-preserving private range query protocol (PPP), which uses bounding-box PIR (bbPIR) to trade access pattern privacy for flexible privacy and high efficiency, and satisfies various quality of service (QoS) requirements. To our best knowledge, PPP is the first to achieve personalized search according to owner-specified privacy-cost tradeoff. Furthermore, PPP is secure against semi-honest adversaries under known ciphertext model. Experimental results on real-world datasets show that PPP is efficient and able to achieve diverse QoS requirements.