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A Personalized Multi-keyword Ranked Search Method Over Encrypted Cloud Data

  • Xue Tian
  • Peisong Shen
  • Tengfei Yang
  • Chi Chen
  • Jiankun HuEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 235)

Abstract

Due to data privacy considerations, the data owners usually encrypt their documents before outsourcing to the cloud. The ability to search the encrypted documents is of great importance. Existing methods usually use the keywords to express users’ query intention, however it’s difficult for the users to construct a good query without the knowledge of document collection. This paper proposes a personalized ciphertext retrieval method based on relevance feedback, which utilizes user interaction to improve the correlation with the search results. The users only need to determine the relevance of the documents instead of constructing a good query, which can greatly improve the users query satisfaction. The selected IEEE published papers are taken as a sample of the experiment. The experimental results show that the proposed method is efficient and could raise the users’ satisfaction. Compared with MRSE-HCI method, our method could achieve higher precision rate and equally high efficiency performance.

Keywords

Cloud computing Ciphertext search Multi-keyword search Relevance feedback 

References

  1. 1.
    Fu, Z., Sun, X., Ji, S., Xie, G.: Towards efficient content-aware search over encrypted outsourced data in cloud. In: IEEE INFOCOM 2016-the 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE, April 2016Google Scholar
  2. 2.
    Li, H., Yang, Y., Luan, T.H., Liang, X., Zhou, L., Shen, X.S.: Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data. IEEE Trans. Dependable Secure Comput. 13(3), 312–325 (2016)CrossRefGoogle Scholar
  3. 3.
    Fu, Z., Ren, K., Shu, J., Sun, X., Huang, F.: Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Trans. Parallel Distrib. Syst. 27(9), 2546–2559 (2016)CrossRefGoogle Scholar
  4. 4.
    Song, D.X., Wagner, D., Perrig, A.: Practical techniques for searches on encrypted data. In: Proceedings of 2000 IEEE Symposium on Security and Privacy, S&P 2000, pp. 44–55. IEEE (2000)Google Scholar
  5. 5.
    Boneh, D., Di Crescenzo, G., Ostrovsky, R., Persiano, G.: Public key encryption with keyword search. In: Cachin, C., Camenisch, J.L. (eds.) EUROCRYPT 2004. LNCS, vol. 3027, pp. 506–522. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24676-3_30CrossRefGoogle Scholar
  6. 6.
    Goh, E.J.: Secure indexes. IACR Cryptology ePrint Archive 2003, 216 (2003)Google Scholar
  7. 7.
    Wang, C., Cao, N., Li, J., Ren, K., Lou, W.: Secure ranked keyword search over encrypted cloud data. In: 2010 IEEE 30th International Conference on Distributed Computing Systems (ICDCS), pp. 253–262. IEEE, June 2010Google Scholar
  8. 8.
    Zhang, B., Zhang, F.: An efficient public key encryption with conjunctive-subset keywords search. J. Netw. Comput. Appl. 34(1), 262–267 (2011)CrossRefGoogle Scholar
  9. 9.
    Cao, N., Wang, C., Li, M., Ren, K., Lou, W.: Privacy-preserving multi-keyword ranked search over encrypted cloud data. In: Proceedings IEEE INFOCOM, pp. 829–837. IEEE Press, New York (2011)Google Scholar
  10. 10.
    Tian, X., Zhu, X., Shen, P., Chen, C., Zou, H.: Efficient ciphertext search method based on similarity search tree. J. Softw. 27(6), 1566–1576 (2016). (in Chinese)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Chen, C., Zhu, X., Shen, P., Hu, J., Guo, S., Tari, Z., Zomaya, A.Y.: An efficient privacy-preserving ranked keyword search method. IEEE Trans. Parallel Distrib. Syst. 27(4), 951–963 (2016)CrossRefGoogle Scholar
  12. 12.
    Chen, C., Zhu, X., Shen, P., Hu, J.: A hierarchical clustering method for big data oriented ciphertext search. In: 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 559–564. IEEE, April 2014Google Scholar
  13. 13.
    White, D.A., Jain, R.: Similarity indexing with the SS-tree. In: Proceedings of the Twelfth International Conference on Data Engineering, pp. 516–523. IEEE, February 1996Google Scholar
  14. 14.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. Wiley, Hoboken (2009)zbMATHGoogle Scholar
  15. 15.
    Witten, I.H., Moffat, A., Bell, T.C.: Managing Gigabytes: Compressing and Indexing Documents and Images. Morgan Kaufmann, Burlington (1999)zbMATHGoogle Scholar
  16. 16.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, vol. 1, no. 1, p. 496. Cambridge University Press, Cambridge (2008)Google Scholar
  17. 17.
    Wong, W.K., Cheung, D.W.L., Kao, B., Mamoulis, N.: Secure kNN computation on encrypted databases. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 139–152. ACM, June 2009Google Scholar
  18. 18.
    Sun, W., Wang, B., Cao, N., Li, M., Lou, W., Hou, Y.T., Li, H.: Privacy-preserving multi-keyword text search in the cloud supporting similarity-based ranking. In: Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security, pp. 71–82. ACM, May 2013Google Scholar
  19. 19.
    Shen, P., Chen, C., Tian, X., Tian, J.: A similarity evaluation algorithm and its application in multi-keyword search on encrypted cloud data. In: Military Communications Conference, Milcom 2015, pp. 1218–1223. IEEE (2015)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Xue Tian
    • 1
    • 2
  • Peisong Shen
    • 1
    • 2
  • Tengfei Yang
    • 1
    • 2
  • Chi Chen
    • 1
    • 2
  • Jiankun Hu
    • 3
    Email author
  1. 1.State Key Laboratory of Information Security, Institute of Information EngineeringCASBeijingChina
  2. 2.School of Cyber SecurityThe University of Chinese Academy of SciencesBeijingChina
  3. 3.Cyber Security Lab, School of Engineering and ITUniversity of New South Wales at the Australian Defence Force AcademyCanberraAustralia

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