Efficiency-Improved Privacy-Preserving Weighted Similarity Query over Outsourced eHealthcare Data
Yandong Zheng, Rongxing Lu, Songnian Zhang, Hui Zhu, Fengwei Wang · GLOBECOM 2022 - 2022 IEEE Global Communications Conference · 2022
Weighted similarity query has been an essential primitive to enable personalized disease diagnosis in eHealthcare. With the prevalence of cloud computing, a new paradigm is to outsource weighted similarity range query services to the cloud. Meanwhile, the query services are usually processed over encrypted data considering data privacy. Although many existing schemes are available to achieve privacy-preserving weighted similarity query over encrypted data, they have some security and query efficiency drawbacks. This paper addresses this problem by proposing an efficient and privacy-preserving weighted similarity range query scheme. First, we employ a k-d tree to index the outsourced dataset and present a k-d tree based weighted similarity range query algorithm. Then, we propose our scheme by applying the MASPE scheme to protect the privacy of the k-d tree based weighted similarity queries. Privacy preservation of our scheme is proved through security analysis. Efficiency improvement is confirmed by the extensive experiments that indicate that our scheme improves 7 x query efficiency than the state-of-the-art scheme.