Privacy-Preserving Top-k Spatio-Textual Similarity Join

With the development of location-based services, spatio-textual similarity join has attracted much research attention from academic and industrial communities following the study of spatio-textual data processing. To offload the computation and storage burden of spatio-textual similarity join, outsourcing the data processing and storage to the public cloud can achieve great cost savings, however, may cause serious privacy concerns. To this end, in this paper, we first define and solve the privacy-preserving spatio-textual similarity join problem and propose two novel secure similarity join schemes. As the baseline, we first present a straightforward scheme applying Asymmetric Inner Product Encryption (AIPE) to facilitate the data encryption and similarity calculation in ciphertext. To improve the efficiency of the basic scheme, we further propose an optimized secure top-k spatio-textual similarity join scheme by constructing a secure index based on the hybrid Locality-Sensitive Hashing (LSH). Through matching the encrypted hash values over the secure index to narrow down the candidates, the similarity join results can be efficiently retrieved from the candidate pairs. Comprehensive analysis of the proposed schemes is provided in terms of computational complexity and security guarantees, and extensive experimental results on real and synthetic datasets show the performance of our schemes.