European Symposium on Research in Computer Security

Computer Security -- ESORICS 2015 pp 40-60 | Cite as

Enabling Privacy-Assured Similarity Retrieval over Millions of Encrypted Records

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9327)


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.


Cloud security Encrypted storage Similarity retrieval 



This work was supported in part by Research Grants Council of Hong Kong (Project No. CityU 138513), grant from City University of Hong Kong (Project No. 7004279), and an AWS in Education Research Grant award.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xingliang Yuan
    • 1
  • Helei Cui
    • 1
  • Xinyu Wang
    • 1
  • Cong Wang
    • 1
  1. 1.City University of Hong KongKowloonHong Kong

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