Privacy-preserving spatial keyword query with exclusion keywords and multi-preference support in road networks

Existing spatial keyword query methods mainly focus on the relevance between interest objects and keywords, while paying relatively limited attention to multiple preference scenarios in road network environments. To overcome the limitations of existing methods in addressing spatial keyword queries that comprehensively consider exclusion keywords and users’ multiple preferences in road networks, as well as to protect user privacy, this paper proposes a privacy-preserving spatial keyword query method with exclusion keywords and multiple preferences in road networks. The method uses a DCEIG-tree pruning algorithm to obtain a preference queue, selects candidate groups from feature objects, allocates them via a dominance-counter-based algorithm, scores interest objects by a scoring function, ranks and returns the optimal set, and adopts differential privacy for protection. Theoretical analysis and experimental results verify the method’s effectiveness in query efficiency, accuracy, and privacy usability.