Improving file locality in multi-keyword top-k search based on clustering
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Nowadays, fast growing number of users and business are motivated to outsource their private data to public cloud servers. Taking into consideration security issues, private data should be encrypted before being outsourced to remote servers, though this makes traditional plaintext keyword search rather difficult. For this reason, there exists an urgent need of an efficient and secure searchable encryption technology. In this paper, an affinity propagation (AP) K-means clustering method (CAK-means, a combination of AP and K-means clustering) is proposed to realize fast searchable encryption in Big Data environments. CAK-means clustering utilizes affinity propagation to initialize K-means clustering, thereby making the clustering process faster, stable and effectively improving the initial clustering center quality of the K-means. As the AP algorithm identifies the clustering center with much lower errors than other methods, it significantly improves the search accuracy. Simultaneously, the related files in one cluster are stored at the contiguous locality of disks which will substantially improve the file locality and speedup the read and write disk I/O. Additionally, the coordinated matching measure is utilized to support accurate ranking of search results. Experimental results show that the proposed CAK-means-based multi-keyword ranked searchable encryption scheme (MRSE-CAK) has higher search efficiency and accuracy while simultaneously ensuring equivalent security.
KeywordsSearchable symmetric encryption CAK-means clustering File locality Multi-keyword Ranked search
This work was supported by the Natural Science Foundation of China (Nos. 61602118, 61572010 and 61472074), Fujian Normal University Innovative Research Team (No. IRTL1207), Natural Science Foundation of Fujian Province (Nos. 2015J01240, 2017J01738), Science and Technology Projects of Educational Office of Fujian Province (No. JK2014009), and Fuzhou Science and Technology Plan Project (No. 2014-G-80).
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Conflict of interest
All authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Asharov G, Naor M, Segev G, et al (2016) Searchable symmetric encryption: optimal locality in linear space via two-dimensional balanced allocations. In: Proceedings of the international conference on ACM symposium on theory of computing, Cambridge, MA, USA, pp 1101–1114Google Scholar
- Cash D, Tessaro S (2014) The locality of searchable symmetric encryption. In: Proceedings of the international conference on the theory and applications of cryptographic techniques, Copenhagen, Denmark, pp 351–368Google Scholar
- Chen L, Qiu L, Li K-C, Zhou S (2018) A secure multi-keyword ranked search over encrypted cloud data against memory leakage attack. J Internet Technol 19(1):179–188Google Scholar
- Curtmola R, Garay J, Kamara S, et al (2006) Searchable symmetric encryption: improved definitions and efficient constructions. In: Proceedings of the international conference on ACM conference on computer and communications security, Alexandria, VA, USA, pp 79–88Google Scholar
- Demertzis I, Papamanthou C (2017) Fast searchable encryption with tunable locality. In: Proceedings of the international conference ACM international conference on management of data, Chicago, Illinois, USA, pp 1053–1067Google Scholar
- Ishai Y, Kushilevitz E, Ostrovsky R (2006) Cryptography from anonymity. In: Proceedings of the international conference on foundations of computer science, Washington, DC, USA, pp 239–248Google Scholar
- Kamara S, Moataz T (2017) Boolean searchable symmetric encryption with worst-case sub-linear complexity. In: Proceedings of the international conference on the theory and applications of cryptographic techniques, Paris, France, pp 94–124Google Scholar
- MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the international conference on Berkeley symposium on mathematical statistics and probability, California, USA, pp 281–297Google Scholar
- Miers I, Mohassel P (2017) IO-DSSE: scaling dynamic searchable encryption to millions of indexes by improving locality. In: Proceedings of the international conference on network and distributed system security symposium, San Diego, California, pp 1–13Google Scholar
- Wang B, Yu S, Lou W, et al (2014) Privacy-preserving multi-keyword fuzzy search over encrypted data in the cloud. In: Proceedings of the international conference on computer communications, Toronto, Canada, pp 2112–2120Google Scholar
- Zhu Y, Yu J, Jia C (2009) Initializing K-means clustering using affinity propagation. In: Proceedings of the international conference on hybrid intelligent systems, Shenyang, China, pp 338–343Google Scholar