Secure Reverse k-Nearest Neighbours Search over Encrypted Multi-dimensional Databases

Abstract

The reverse k-nearest neighbours search is a fundamental primitive in multi-dimensional (i.e. multi-attribute) databases with applications in location-based services, online recommendations, statistical classification, pat-tern recognition, graph algorithms, computer games development, and so on. Despite the relevance and popularity of the query, no solution has yet been put forward that supports it in encrypted databases while protecting at the same time the privacy of both the data and the queries. With the outsourcing of massive datasets in the cloud, it has become urgent to find ways of ensuring the fast and secure processing of this query in untrustworthy cloud computing. This paper presents searchable encryption schemes which can efficiently and securely enable the processing of the reverse k-nearest neighbours query over encrypted multi-dimensional data, including index-based search schemes which can carry out fast query response that preserves data confidentiality and query privacy. The proposed schemes resist practical attacks operating on the basis of powerful background knowledge and their efficiency is confirmed by a theoretical analysis and extensive simulation experiments.