Enabling Privacy-Assured Similarity Retrieval over Millions of Encrypted Records

Searchable symmetric encryption (SSE) has been studied extensively for its full potential in enabling exact-match queries on encrypted records. Yet, situations for similarity queries remain to be fully explored. In this paper, we design privacy-assured similarity search schemes over millions of encrypted high-dimensional records. Our design employs locality-sensitive hashing (LSH) and SSE, where the LSH hash values of records are treated as keywords fed into the framework of SSE. As direct combination of the two does not facilitate a scalable solution for large datasets, we then leverage a set of advanced hash-based algorithms including multiple-choice hashing, open addressing, and cuckoo hashing, and craft a high performance encrypted index from the ground up. It is not only space efficient, but supports secure and sufficiently accurate similarity search with constant time. Our designs are proved to be secure against adaptive adversaries. The experiment on 10 million encrypted records demonstrates that our designs function in a practical manner. This paper was presented at the 20th European Symposium on Research in Computer Security (ESORICS 2015), Vienna, Austria, September 21-25, 2015. Supervisor: Dr Cong WANG Research Interests: Cloud Computing; Data and Computation Outsourcing Security; Mobile Security.