Achieving Privacy-Preserving Weighted Similarity Range Query over Outsourced eHealthcare Data
Yandong Zheng, Rongxing Lu, Songnian Zhang · 2022
Similarity queries have been widely employed to offer more effective medical care to patients in eHealthcare. As a special query, similarity query with user-defined weights, which allows users (i.e., doctors in eHealthcare) to define the weight for the distance metric, has received particular interest recently. In order to make the weighted similarity query service more flexible and reliable, healthcare centers tend to outsource the healthcare data and the corresponding similarity query service to a powerful cloud. However, due to privacy concerns, healthcare centers usually demand to encrypt the data before outsourcing them to the cloud. Although some existing privacy-preserving similarity query schemes can be adapted to handle weighted similarity range queries, they may face issues in either the practicality or the accuracy of query results. Aiming at addressing these issues, in this paper, we design an efficient privacy-preserving weighted similarity range query (EPW-Sim) scheme, which is practical and can return accurate query results. Specifically, we first discover a lower bound for the distance metric, i.e., weighted Euclidean distance, and further leverage the lower bound as a filtration condition to design an efficient weighted similarity range query algorithm. Second, we apply a modified asymmetric-scalar-product encryption (MASPE) scheme to preserve the privacy of the designed algorithm and propose our EPW-Sim scheme. Finally, we analyze the security of our scheme and conduct experiments to validate its efficiency, and the results demonstrate that our scheme is privacy-preserving and efficient.