Maintaining Privacy for Multi-keyword Search on Encrypted Data: A Survey
Abstract
Cloud computing is rising as a promising pattern for information outsourcing and high-quality services. The data owners prefer to outsource their data to the cloud server in order to reduce data management cost and storage facility. Data encryption and compression is used to maintain the security and privacy of documents and also to reduce the cloud storage space. The encrypted documents are stored on cloud server. The similarity between the documents will be hiding in the process of encryption, which will lead to the search accuracy performance degradation. The amount of data stored on cloud server increases per day. It is challenging to design search technique on encrypted documents that can provide the reliable online information retrieval on large volume of encrypted data. We propose a cosine similarity clustering to support more search semantics and fast search within the large volume of data. The proposed cosine similarity approach clusters the documents based on their cosine similarity value. We also propose a method which can maintain and utilize the relationship between documents to increase the speed of search.
Keywords
Full Text:
PDFReferences
Chi Chen, Xiaojie Zhu, Peisong Shen, J.Hu S.Gue, Z.Tari and Albert Y. Zomaya, “An Efficient Privacy Preserving Ranked Keyword Search Method,” IEEE Transactions on Parallel and Distributed Systems, 2015.
Jiadi Yu, Peng Lu, Yanmin Zhu, Guangtao Xue and Minglu Li, “Toward Secure Multi-Keyword Top-K Retrieval over Encrypted Cloud data, ”IEEE Transactions on Dependable and secure computing, Vol. 10, No. 4 July/August 2013.
N. Cao, C. Wang, M. Li, K. Ren, and W. J. Lou, “Privacy-Preserving Multi-keyword Ranked Search over Encrypted Cloud Data,” in Proc. IEEE INFOCOM, Shanghai, China, 2011, pp. 829-837.
Ruksana Akter, Yoojin Chung, “An Evolutionary Approach for Document Clustering,” 2013 International Conference on Electronic Engineering and Computer Science.
Juan Ramos, Department of Computer Science, Rutgers University, 23515 BPO Way, Piscataway, NJ, 08855, “Using TF-IDF to Determine Word Relevance in Document Queries”
Ning Cao, Jin Li, Kui Ren, “Secure Ranked Keyword Search over Encrypted Cloud Data,”IEEE 30th International Conference on Distributed Computing System, 2010
M. Bellare, A. Boldyreva, and A. O’Neill, “Deterministic and efficiently searchable encryption,” in Proc. CRYPTO, Santa Barbara, CA, 2007, pp. 535-552.
D. X. D. Song, D. Wagner, and A. Perrig, ”Practical techniques for searches on encrypted data,” in Proc. S & P, BERKELEY, CA, 2000, pp. 44-55.
W. Sun, B. Wang, N. Cao, M. Li, W. Lou, Y. T. Hou, andH. Li, ”Privacy-preserving multi-keyword text search in the cloud supporting similarity-based ranking,” in Proc. ASIACCS, Hangzhou, China, 2013, pp. 71-82.
F. Li, M. Hadjieleftheriou, G. Kollios, and L. Reyzin, ”Dynamic authenticated index structures for outsourced databases,” inProc. ACM SIGMOD, Chicago, IL, USA, 2006, pp. 121-132.
D. Boneh, G. Di Crescenzo, R. Ostrovsky, and G. Persiano, “Public key encryption with keyword search,” in Proc. EUROCRYPT, Interlaken, SWITZERLAND, 2004, pp. 506-522.
Y. C. Chang, and M. Mitzenmacher, ”Privacy preserving keyword searches on remote encrypted data,” in Proc. ACNS, Columbia Univ, New York, NY, 2005, pp. 442-455.
R. Curtmola, J. Garay, S. Kamara, and R. Ostrovsky, ”Searchable symmetric encryption: improved definitions and efficient constructions,” in Proc. ACM CCS, Alexandria, Virginia, USA, 2006, pp. 79-88.
D. Boneh, and B. Waters, ”Conjunctive, subset, and range queries on encrypted data,” in Proc. TCC, Amsterdam, NETHERLANDS, 2007, pp. 535-554.
A. Swaminathan, Y. Mao, G. M. Su, H. Gou, A. Varna, S. He, M. Wu, and D. Oard, ”Confidentiality-Preserving Rank-Ordered Search,” in Proc. ACM StorageSS, Alexandria, VA, 2007, pp. 7-12.
H. Pang, J. Shen, and R. Krishnan, Privacy-Preserving Similarity-Based Text Retrieval, ACM Trans. Internet. Technol., vol. 10, no. 1, pp. 39, Feb. 2010.
Y. H. Hwang, and P. J. Lee, ”Public key encryption with conjunctive keyword search and its extension to a multi-user system,” in Proc. Pairing, Tokyo, JAPAN, 2007, pp. 2-22.
C. Chen, X. J. Zhu, P. S. Shen, and J. K. Hu, ”A Hierarchical Clustering Method For Big Data Oriented Ciphertext Search,” presented at Proc. BigSecurity, Toronto, Canada, Apr. 27-May. 2, 2014.
S. C. Yu, C. Wang, K. Ren, and W. J. Lou, ”Achieving Secure, Scalable, and Fine-grained Data Access Control in Cloud Computing,” in Proc. IEEE INFOCOM, San Diego, CA, 2010, pp. 1-9.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.