Privacy-Preserving Direction-Aware Why-Not Spatial Keyword Top-k Queries on Cloud Platform
Yiping Teng, Huan Wang, Jiawei Qi, Miao Li, Chunlong Fan, Li Xu · 2023
Direction-aware why-not spatial keyword top-k queries, which aim to retrieve a refined spatial keyword query that includes the missing objects with smallest cost on search direction, have recently received considerable attention from the database community. To offload the data management, when the why-not spatial keyword query processing services are motivated to be outsourced to the cloud for cost savings and flexibility, it may cause serious privacy concerns. To this end, in this paper, we first define and address the problem of privacy-preserving direction-aware why-not spatial keyword top-k queries. To support direction computations in ciphertext, we first propose two novel secure computation protocols, i.e., Secure Modulus Protocol and Secure Angle Computation Protocol. Based on the proposed protocols, to facilitate the secure why-not query processing, we further present a Secure Direction-Aware Why-Not Spatial Keyword Top-k Query (SDAWNkQ) approach for retrieving the refined queries with the smallest penalty by obtaining the best approximate refined directions. Thorough analysis shows the security and computational complexity of our approach, and extensive experimental results on both real and synthetic datasets further demonstrate the query performance of our approach.