Secure Semantic Search Based on Two-Level Index Over Encrypted Cloud
With the rapid development of the IoT and the mobile applications, users are tending to outsource the data to the cloud servers. Thus, the encrypted data search on the cloud is very important. Now there are a lot existed keyword search schemes in which the relationship between the size of the file set and the search time is linear. In order to solve this problem, we propose a semantic retrieval framework for central word expansion based on two-level index. In this paper, we have taken a new approach for index construction - the two-level index to ensure that the retrieval time is not affected by the file size. In order to better meet the semantic requirements of user queries, we introduced the central word expansion technology to further improve the accuracy of the search. The main idea of central keyword extension semantic search (CKESS) based on two-level index is that match the expanded central keyword with index firstly, then compute the similarity between the query and the index under the first matching result, and finally return the result with the highest similarity. Our proposed solution meets the privacy protection requirements under two different threat models. Through the experiment of the real data set, we prove that our scheme is efficient, accurate and secure.
KeywordsTwo-level index Central word expansion Semantic search
This work is supported by the NSFC (61772283, 61672294, U1536206, 61502242, U1405254, 61602253), BK20150925, R2017L05, PAPD fund, Project funded by China Postdoctoral Science Foundation, Major Program of the National Social Science Fund of China (17ZDA092), Qing Lan Project, and Meteorology Soft Sciences Project.
- 1.Goh, E.J.: Secure indexes. Submission (2003)Google Scholar
- 6.Har-Peled, S., Indyk, P., Motwani, R.: Approximate nearest neighbor: towards removing the curse of dimensionality. In: ACM Symposium on Theory of Computing, pp. 604–613 (1998)Google Scholar
- 10.Lin, D.: An information-theoretic definition of similarity. In: Fifteenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc., pp. 296–304 (1998)Google Scholar
- 11.Wong, W.K., Cheung, D.W., Kao, B., Mamoulis, N.: Secure kNN computation on encrypted databases. In: ACM SIGMOD International Conference on Management of Data. ACM, pp. 139–152 (2009)Google Scholar
- 12.Yang, J., Liu, Z., Li, J., Jia, C., Cui, B.: Multi-key searchable encryption without random Oracle. In: International Conference on Intelligent NETWORKING and Collaborative Systems. IEEE, pp. 79–84 (2015)Google Scholar
- 14.Wang, B., Li, M., Wang, H., Li, H.: Circular range search on encrypted spatial data. In: Communications and Network Security, pp. 182–190. IEEE (2015)Google Scholar
- 15.Li, J., Wang, Q., Wang, C., Cao, N., Ren, K., Lou, W.: Fuzzy keyword search over encrypted data in cloud computing. In: Conference on Information Communications, pp. 441–445. IEEE Press (2010)Google Scholar
- 16.Chuah, M., Hu, W.: Privacy-aware BedTree based solution for fuzzy multi-keyword search over encrypted data. In: International Conference on Distributed Computing Systems Workshops. IEEE Computer Society, pp. 273–281 (2011)Google Scholar
- 17.Kuzu, M., Islam, M.S., Kantarcioglu, M.: Efficient similarity search over encrypted data. In: IEEE International Conference on Data Engineering, pp. 1156–1167. IEEE (2012)Google Scholar
- 20.Sun, X., Zhu, Y., Xia, Z., et al.: Secure keyword-based ranked semantic search over encrypted cloud data. In: The International Conference on Multimedia, Computer Graphics and Broadcasting, pp. 271–283 (2013)Google Scholar