Advertisement

Class Indistinguishability for Outsourcing Equality Conjunction Search

  • Weipeng LinEmail author
  • Ke Wang
  • Zhilin Zhang
  • Ada Waichee Fu
  • Raymond Chi-Wing Wong
  • Cheng Long
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11513)

Abstract

Searchable symmetric encryption (SSE) enables a remote cloud server to answer queries directly over encrypted data on a client’s behalf, therefore, relieves the resource limited client from complicated data management tasks. Two key requirements are a strong security guarantee and a sub-linear search performance. The bucketization approach in the literature addresses these requirements at the expense of downloading many false positives or requiring the client to search relevant bucket ids locally, which limits the applicability of the method. In this paper, we propose a novel approach CLASS to meet these requirements for equality conjunction search while minimizing the client work and communication cost. First, we generalize the standard ciphertext indistinguishability to partitioned data, called class indistinguishability, which provides a level of ciphertext indistinguishability similar to that of bucketization but allows the cloud server to perform search of relevant data and filtering of false positives. We present a construction achieving these goals through a two-phase search algorithm for a query. The first phase finds a candidate set through a sub-linear search. The second phase finds the exact query result using a linear search applied to the candidate set. Both phases are performed by the server and are implemented by plugging in existing search methods. The experiment results on large real-world data sets show that our approach outperforms the state-of-the-art.

Keywords

Searchable encryption Equality conjunction search Sub-linear search 

Notes

Acknowledgments

This work was partially supported by a Discovery Grant from Canada’s NSERC.

References

  1. 1.
    IPUMS US census data set. https://www.ipums.org
  2. 2.
    Ballard, L., Kamara, S., Monrose, F.: Achieving efficient conjunctive keyword searches over encrypted data. In: Qing, S., Mao, W., López, J., Wang, G. (eds.) ICICS 2005. LNCS, vol. 3783, pp. 414–426. Springer, Heidelberg (2005).  https://doi.org/10.1007/11602897_35CrossRefGoogle Scholar
  3. 3.
    Boldyreva, A., Chenette, N., Lee, Y., O’Neill, A.: Order-preserving symmetric encryption. In: Joux, A. (ed.) EUROCRYPT 2009. LNCS, vol. 5479, pp. 224–241. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-01001-9_13CrossRefGoogle Scholar
  4. 4.
    Bösch, C.T., Hartel, P.H., Jonker, W., Peter, A.: A survey of provably secure searchable encryption. ACM Comput. Surv., 1125–1134 (2014)Google Scholar
  5. 5.
    Cash, D., Jarecki, S., Jutla, C., Krawczyk, H., Roşu, M.-C., Steiner, M.: Highly-scalable searchable symmetric encryption with support for boolean queries. In: Canetti, R., Garay, J.A. (eds.) CRYPTO 2013. LNCS, vol. 8042, pp. 353–373. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40041-4_20CrossRefGoogle Scholar
  6. 6.
    Ciaccia, P., Patella, M., Zezula, P.: M-tree: an efficient access method for similarity search in metric spaces. In: VLDB (1997)Google Scholar
  7. 7.
    Curtmola, R., Garay, J., Kamara, S., Ostrovsky, R.: Searchable symmetric encryption: improved definitions and efficient constructions. In: CCS (2006)Google Scholar
  8. 8.
    Falls, L.W.: The beta distribution: a statistical model for world cloud cover. J. Geophys. Res. 79, 1261–1264 (1974)CrossRefGoogle Scholar
  9. 9.
    Goh, E.J.: Secure indexes. IACR (2003)Google Scholar
  10. 10.
    Golle, P., Staddon, J., Waters, B.: Secure conjunctive keyword search over encrypted data. In: Jakobsson, M., Yung, M., Zhou, J. (eds.) ACNS 2004. LNCS, vol. 3089, pp. 31–45. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24852-1_3CrossRefGoogle Scholar
  11. 11.
    Hacigümüş, H., Iyer, B., Li, C., Mehrotra, S.: Executing SQL over encrypted data in the database-service-provider model. In: SIGMOD (2002)Google Scholar
  12. 12.
    Hore, B., Mehrotra, S., Canim, M., Kantarcioglu, M.: Secure multidimensional range queries over outsourced data. VLDB 21, 333–358 (2012)CrossRefGoogle Scholar
  13. 13.
    Hore, B., Mehrotra, S., Tsudik, G.: A privacy-preserving index for range queries. In: VLDB (2004)CrossRefGoogle Scholar
  14. 14.
    Ji, X., Mitchell, J.E.: Branch-and-price-and-cut on the clique partitioning problem with minimum clique size requirement. Discrete Optim. 4, 87–102 (2007)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kamara, S., Moataz, T.: Boolean searchable symmetric encryption with worst-case sub-linear complexity. In: Coron, J.-S., Nielsen, J.B. (eds.) EUROCRYPT 2017. LNCS, vol. 10212, pp. 94–124. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-56617-7_4CrossRefGoogle Scholar
  16. 16.
    Li, M., Yu, S., Cao, N., Lou, W.: Authorized private keyword search over encrypted data in cloud computing. In: ICDCS (2011)Google Scholar
  17. 17.
    Lin, W., Wang, K., Zhang, Z., Chen, H.: Revisiting security risks of asymmetric scalar product preserving encryption and its variants. In: ICDCS (2017)Google Scholar
  18. 18.
    Oliveira, S.R., Zaiane, O.R.: Privacy preserving clustering by data transformation. In: SBBD (2003)Google Scholar
  19. 19.
    Popa, R.A., Redfield, C., Zeldovich, N., Balakrishnan, H.: CryptDB: protecting confidentiality with encrypted query processing. In: SOSP (2011)Google Scholar
  20. 20.
  21. 21.
    Stefanov, E., et al.: Path ORAM: an extremely simple oblivious ram protocol. In: CCS (2013)Google Scholar
  22. 22.
    Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10, 557–570 (2002)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Wang, P., Ravishankar, C.V.: Secure and efficient range queries on outsourced databases using \(\hat{R}\)-tree. In: ICDE (2013)Google Scholar
  24. 24.
    Wong, W.K., Cheung, D.W.L., Kao, B., Mamoulis, N.: Secure kNN computation on encrypted databases. In: SIGMOD (2009)Google Scholar
  25. 25.
    Yi, X., Kaosar, M.G., Paulet, R., Bertino, E.: Single-database private information retrieval from fully homomorphic encryption. TKDE, 1125–1134 (2013)Google Scholar
  26. 26.
    Zhang, Y., Katz, J., Papamanthou, C.: All your queries are belong to us: the power of file-injection attacks on searchable encryption. Usenix (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Weipeng Lin
    • 1
    Email author
  • Ke Wang
    • 1
  • Zhilin Zhang
    • 1
  • Ada Waichee Fu
    • 2
  • Raymond Chi-Wing Wong
    • 3
  • Cheng Long
    • 4
  1. 1.Simon Fraser UniversityVancouverCanada
  2. 2.Chinese University of Hong KongHong KongChina
  3. 3.Hong Kong University of Science and TechnologyHong KongChina
  4. 4.Nanyang Technological UniversitySingaporeSingapore

Personalised recommendations