Searchable Encryption over Feature-Rich Data

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, searchable symmetric encryption (SSE) schemes have been proposed. However, m...

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Vydáno v:	IEEE transactions on dependable and secure computing Ročník 15; číslo 3; s. 496 - 510
Hlavní autoři:	Wang, Qian, He, Meiqi, Du, Minxin, Chow, Sherman S. M., Lai, Russell W. F., Zou, Qin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Washington IEEE 01.05.2018 IEEE Computer Society
Témata:	Boolean algebra Cloud storage Cybersecurity Data encryption Data storage Encryption homomorphic encryption Indexes Keywords Multimedia Multimedia communication Performance indices proximity search searchable encryption Searching Servers similarity search Sound filters
ISSN:	1545-5971, 1941-0018
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Abstract	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, searchable symmetric encryption (SSE) schemes have been proposed. However, many schemes construct indexes based on keyword-file pairs and focus on boolean expressions of exact keyword matches. Moreover, most dynamic SSE schemes 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 real-world high-dimensional datasets, and achieved a search quality of 99 percent 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.
AbstractList	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, searchable symmetric encryption (SSE) schemes have been proposed. However, many schemes construct indexes based on keyword-file pairs and focus on boolean expressions of exact keyword matches. Moreover, most dynamic SSE schemes 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 real-world high-dimensional datasets, and achieved a search quality of 99 percent 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.
Author	Wang, Qian Zou, Qin Du, Minxin He, Meiqi Chow, Sherman S. M. Lai, Russell W. F.
Author_xml	– sequence: 1 givenname: Qian orcidid: 0000-0002-8967-8525 surname: Wang fullname: Wang, Qian email: qianwang@whu.edu.cn organization: State Key Lab of Software Engineering, School of Computer Science, Wuhan University, Wuhan, Hubei, China – sequence: 2 givenname: Meiqi surname: He fullname: He, Meiqi email: mqhe@hku.hk organization: Department of Computer Science, The University of Hong Kong, Hong Kong – sequence: 3 givenname: Minxin orcidid: 0000-0001-6620-6923 surname: Du fullname: Du, Minxin email: duminxin@whu.edu.cn organization: State Key Lab of Software Engineering, School of Computer Science, Wuhan University, Wuhan, Hubei, China – sequence: 4 givenname: Sherman S. M. surname: Chow fullname: Chow, Sherman S. M. email: sherman@ie.cuhk.edu.hk organization: Department of Information Engineering, Chinese University of Hong Kong, Hong Kong – sequence: 5 givenname: Russell W. F. surname: Lai fullname: Lai, Russell W. F. email: wflai@ie.cuhk.edu.hk organization: Department of Information Engineering, Chinese University of Hong Kong, Hong Kong – sequence: 6 givenname: Qin surname: Zou fullname: Zou, Qin email: qzou@whu.edu.cn organization: State Key Lab of Software Engineering, School of Computer Science, Wuhan University, Wuhan, Hubei, China
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SubjectTerms	Boolean algebra Cloud storage Cybersecurity Data encryption Data storage Encryption homomorphic encryption Indexes Keywords Multimedia Multimedia communication Performance indices proximity search searchable encryption Searching Servers similarity search Sound filters
Title	Searchable Encryption over Feature-Rich Data
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