Enhancing Privacy-Preserving Cloud Database Querying by Preventing Brute Force Attacks

Authors: Ambika Vishal Pawar, Ajay Dani

Abstract:

Considering the complexities involved in Cloud computing, there are still plenty of issues that affect the privacy of data in cloud environment. Unless these problems get solved, we think that the problem of preserving privacy in cloud databases is still open. In tokenization and homomorphic cryptography based solutions for privacy preserving cloud database querying, there is possibility that by colluding with service provider adversary may run brute force attacks that will reveal the attribute values.

In this paper we propose a solution by defining the variant of K –means clustering algorithm that effectively detects such brute force attacks and enhances privacy of cloud database querying by preventing this attacks.

Keywords: Privacy, Database, Cloud Computing, Clustering, K-means, Cryptography.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336458

References:

[1] Kui Ren, Cong Wang, and Qian Wang, Security Challenges for the Public Cloud, Internet Computing, IEEE (Volume: 16, Issue: 1).
[2] Y. Lu and G. Tsudik, Privacy-Preserving Cloud Database Querying, Journal of Internet Services and Information Security (JISIS), Vol. 1 No. 4, November 2011.
[3] Siani Pearson, Yun Shen, Miranda Mowbray, A Privacy Manager for Cloud Computing, First International Conference, CloudCom 2009, Beijing, China, December 1-4, 2009. Proceedings.
[4] Jian Wang, Yan Zhao; Shuo Jiang; Jiajin Le, Providing privacy preserving in cloud computing, International Conference on Test and Measurement, 2009. 213-216.
[5] Miao Zhou, Yi Mu, Willy Susilo Jun Yan, Liju Dong, Privacy enhanced data outsourcing in the cloud, Journal of Network and Computer Applications, 35 (2012) 1367–1373.
[6] Qin Liu, Guojun Wang, Jie Wu, Secure and privacy preserving keyword searching for cloud storage services, Journal of Network and Computer Applications 35 (2012) 927–933.
[7] Haibo Hu; Jianliang Xu; Chushi Ren; Byron Choi, Processing private queries over untrusted data cloud through privacy homomorphism , IEEE 27th International Conference on Data Engineering (ICDE), 2011.
[8] Marten Van Dijk, Ari Juels, On the Impossibility of Cryptography Alone for Privacy-Preserving Cloud Computing.
[9] Dr. Alexander Benlian, Prof. Dr. Thomas Hess, Prof. Dr. Peter Buxmann, Drivers of SaaS-Adoption – An Empirical Study of Different Application Type, Business & Information Systems Engineering October 2009, Volume 1, Issue 5, pp 357-369.
[10] Shucheng Yu Cong Wang ; Kui Ren ; Wenjing Lou, Achieving Secure, Scalable, and Fine-grained Data Access Control in Cloud Computing, INFOCOM, 2010 Proceedings IEEE.
[11] Adi Shamir, Identity-Based Cryptosystems and Signature Schemes, Proceedings of CRYPTO 84.
[12] Amit Sahai, Brent Waters, Fuzzy Identity-Based Encryption, 24th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Aarhus, Denmark, May 22-26, 2005. Proceedings.
[13] Vipul Goyal,Omkant Pandey, Amit Sahai, Brent Waters , Attribute-Based Encryption for Fine-Grained Access Control of Encrypted Data, Proceeding CCS '06 Proceedings of the 13th ACM conference on Computer and communications security Pages 89 - 98 ,ACM New York, NY.
[14] John Bethencourt, Amit Sahai, Brent Waters, Ciphertext-Policy Attribute-Based Encryption, IEEE Symposium on Security and Privacy 2007 (SP' 2007)
[15] Agrawal R., Srikant R. Privacy-Preserving Data Mining. ACM SIGMOD Conference, 2000.
[16] Samarati P., SweeneyL. Protecting Privacy when Disclosing Information: k-Anonymity and its Enforcement Through Generalization and Suppression. IEEE Symp. on Security and Privacy, 1998.
[17] Bayardo R. J., Agrawal R. Data Privacy through optimal k-anonymization. ICDE Conference, 2005.
[18] Machanavajjhala A., Gehrke J., Kifer D. l-diversity: Privacy beyond k- anonymity. IEEE ICDE Conference, 2006.
[19] Aggarwal C. C., Yu P. S.: A Condensation approach to privacy preserving data mining. EDBT Conference, 2004.
[20] Aggarwal C. C., Yu P. S.: On Variable Constraints in Privacy-Preserving Data Mining. SIAM Conference, 2005.
[21] Pinkas B.: Cryptographic Techniques for Privacy-Preserving Data Mining. ACM SIGKDD Explorations, 4(2), 2002.
[22] A. Hartigan and M. A. Wong Algorithm AS 136: A K-Means Clustering Algorithm Journal of the Royal Statistical Society. Series C (Applied Statistics) Vol. 28, No. 1 (1979), pp. 100-108.
[23] Mark Junjie Li, Michael K. Ng, Yiu-ming Cheung: Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters, IEEE Transactions on Knowledge and Data Engineering, Vol. 20 No. 11, November 2008.
[24] O. Grayorash, Y. Zhou, and Z, Jorgenssn, Minimum Spanning tree based clustering algorithms, Proc,. of IEEE Inn. Conf Tools with Artificial Intelligence, pp 73-81, 2006.
[25] K. Chidananda Gowda and G. Krishna The Condensed Nearest Neighbor Rule Using the Concept of Mutual Nearest Neighborhood, IEEE Transactions on Information Theory, vol. It-25,no. 4, july 1979.
[26] R. Sibson, SLINK: An optimally efficient algorithm for the single-link cluster method. The Computer Journal, Volume 16, 1973.
[27] D. Defays. An efficient algorithm for a complete link method, The Computer Journal (1977).
[28] Stephen Redmond, Conor Heneghan , A method for initialising the K-means clustering algorithm using kd-trees, ACM Journal of, Pattern Recognition, Volume 28 Issue 8, June, 2007 Pages 965-973.
[29] M. Emre Celebi, , Hassan A. Kingravi , Deterministic Initialization of the K-Means Algorithm Using Hierarchical Clustering, International Journal of Pattern Recognition and Artificial Intelligence, 26(7): 1250018, 2012.
[30] D T Pham, S S Dimov, and C D Nguyen ,’ Selection of K in K-means clustering’, Proc. IMechE Vol. 219 Part C: J. Mechanical Engineering Science.
[31] Sebastian Thrun, Lawrence K. Saul ‘Learning the k in k-means Greg Hamerly’ Advances in Neural Information Processing Systems, Volume 16.
[32] Chris Ding, Xiaofeng He, K-means Clustering via Principal Component Analysis, Proceedings of the 21 st International Confer- ence on Machine Learning, Banff, Canada, 2004.
[33] Greg Hamerly, Charles Elkan , Alternatives to the k-means algorithm that find better clusterings, ACM Proceeding CIKM '02 Proceedings of the eleventh international conference on Information and knowledge management, Pages 600-607.
[34] Guha S., Rastogi R., Shim K. CURE: An efficient clustering algorithm for large databases, Year: 1998 ACM, Source title: SIGMOD Record Volume: 27 Issue: 2 Page: 73-84.
[35] Inderjit S. Dhillon, James Fan , Yuqiang Guan , Efficient Clustering of Very Large Document Collections, Data Mining for Scientific and Engineering Applications, Springer, 31-Oct-2001.
[36] Shiyuan Wang, Divyakant Agrawal, and Amr El Abbadi, A Comprehensive Framework for Secure Query Processing on Relational Data in the Cloud, Proceeding SDM'11 Proceedings of the 8th VLDB international conference on Secure data management Pages 52-69.