Qian Wang , Qian Wang is with the The State Key Lab of Software Engineering, School of Computer Science, Wuhan University, China. (email: firstname.lastname@example.org)
Storage services allow data owners to store their huge amount of potentially sensitive data, such as audios, images, and videos, on remote cloud servers in encrypted form. To enable retrieval of encrypted files of interest, many searchable symmetric encryption (SSE) schemes have been proposed. However, most existing SSE solutions construct indexes based on keyword-file pairs and focus on boolean expressions of exact keyword matches. Moreover, most dynamic SSE solutions cannot achieve forward privacy and reveal unnecessary information when updating the encrypted databases. We tackle the challenge of supporting large-scale similarity search over encrypted feature-rich multimedia data, by considering the search criteria as a high-dimensional feature vector instead of a keyword. Our solutions are built on carefully-designed fuzzy Bloom filters which utilize locality sensitive hashing (LSH) to encode an index associating the file identifiers and feature vectors. Our schemes are proven to be secure against adaptively chosen query attack and forward private in the standard model. We have evaluated the performance of our scheme on various real-world high-dimensional datasets, and achieved a search quality of 99% recall with only a few number of hash tables for LSH. This shows that our index is compact and searching is not only efficient but also accurate.
proximity search, Cloud storage, searchable encryption, homomorphic encryption, similarity search
Q. Wang, M. He, M. Du, S. S. Chow, R. W. Lai and Q. Zou, "Searchable Encryption over Feature-Rich Data," in IEEE Transactions on Dependable and Secure Computing.