EncSIM: an encrypted similarity search service for distributed high-dimensional datasets

Similarity-oriented services serve as a foundation in a wide range of data analytic applications such as machine learning, target advertising, and real-time decisions. Both industry and academia strive for efficient and scalable similarity discovery and querying techniques to handle massive, complex data records in the real world. In addition to performance, data security and privacy become an indispensable criterion in the quality of service due to progressively increased data breaches. To address this serious concern, in this paper, we propose and implement 'EncSIM', an encrypted and scalable similarity search service. The architecture of EncSIM enables parallel query processing over distributed, encrypted data records. To reduce client overhead, EncSIM resorts to a variant of the state-of-the-art similarity search algorithm, called all-pairs locality-sensitive hashing (LSH). We describe a novel encrypted index construction for EncSIM based on searchable encryption to guarantee the security of service while preserving performance benefits of all-pairs LSH. Moreover, EncSIM supports data record addition with a strong security notion. Intensive evaluations on a cluster of Redis demonstrate low client cost, linear scalability, and satisfied query performance of EncSIM.