secure sharing of private data using privacy-preserving data analysis
D S Deepika,R Nandhini,L Nandhini,A Sangeeth Sharmili
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:2 Issue:1 Year: 08 February,2014 Pages:35-42
Abstract
Generally, colliding parties who have private data may conduct privacy-preserving data analysis (PPDA) tasks to learn beneficial data models in a distributed manner. The field of privacy has seen rapid advances in recent years because of the increases in the ability to store data. In particular, recent advances in the data mining field have lead to increased concerns about privacy. While the topic of privacy has been traditionally studied in the context of cryptography and information-hiding, recent emphasis on data mining has lead to renewed interest in the field. In this paper, we will introduce the topic of privacy-preserving data mining. It is often highly valuable for organizations to have their data analyzed by external agents. However, any program that computes on potentially sensitive data may lead to risks leaking information through its output. Differential privacy provides a theoretical framework for processing data while protecting the privacy of individual records in a dataset. Unfortunately, it has seen limited adoption because of the loss in output accuracy, the difficulty in making programs differentially private, lack of mechanisms to describe the privacy budget in a programmer's utilitarian terms. So, in this paper we have proposed how to share private data securely.
Kewords
Privacy secure multiparty computation noncooperative computation
Reference
[1] I. Abraham, D. Dolev, R. Gonen, and J. Halpern. Distributed computing meets game theory: Robust mechanisms for rational secret sharing and multiparty computation. In Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing, pages 53–62. ACM New York, NY, USA, 2006. [2] R. Agrawal and E. Terzi. On honesty in sovereign information sharing. Lecture Notes in Computer Science, 3896:240, 2006. [3] Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules. In VLDB ’94, pages 487–499, Santiago, Chile, September 12-15 1994. VLDB. [4] Shuguo Han and Wee Keong Ng. Preemptive measures against malicious party in privacy-preserving data mining. In SDM, pages 375–386, 2008. [5] Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data. Official Journal of the European Communities, No I.(281):31–50, October 24 1995. [6] B. Chor and E. Kushilevitz. A zero-one law for boolean privacy. In STOC ’89, pages 62–72, New York, NY, USA, 1989. ACM Press. [7] O. Goldreich, S. Micali, and A. Wigderson. How to play any mental game - a completeness theorem for protocols with honest majority. In 19th ACM Symposium on the Theory of Computing, pages 218–229, 1987. [8] www.doe.gov, doe news, feb. 16 2005. [9] Mikhail J. Atallah, Marina Bykova, Jiangtao Li, and Mercan Karahan. Private collaborative forecasting and benchmarking. In Proc. 2d. ACM Workshop on Privacy in the Electronic Society (WPES), Washington, DC, October 28 2004. [10] Keinosuke Fukunaga. Introduction to Statistical Pattern Recognition. Academic Press, San Diego, CA, 1990. [11] Wenliang Du and Zhijun Zhan. Building decision tree classifier on private data. In Chris Clifton and Vladimir Estivill-Castro, editors, IEEE International Conference on Data Mining Workshop on Privacy, Security, and Data Mining, volume 14, pages 1–8, Maebashi City, Japan, December 9 2002. Australian Computer Society. [12]Oded Goldreich. The Foundations of Cryptography, volume 2, chapter General Cryptographic Protocols. Cambridge University Press, 2004. [13] S.D. Gordon and J. Katz. Rational secret sharing, revisited. Lecture Notes in Computer Science, 4116:229, 2006. [14] Joseph Halpern and Vanessa Teague. Rational secret sharing and multiparty computation: extended abstract. In STOC ’04, pages 623– 632, New York, NY, USA, 2004. ACM Press. [15] Standard for privacy of individually identifiable health information. Federal Register, 67(157):53181–53273, August 14 2002. [16] M. Naor, B. Pinkas, and R. Sumner, “Privacy Preserving Auctions and Mechanism Design,” Proc. First ACM Conf. Electronic Commerce, 1999. [17] J. Han and M. Kamber. Data mining: concepts and techniques. The Morgan Kaufmann series in data management systems. Elsevier, 2006. [18] S. Izmalkov, S. Micali, and M. Lepinski. Rational secure computation and ideal mechanism design. In Foundations of Computer Science, 2005. FOCS 2005. 46th Annual IEEE Symposium on, pages 585–594, 2005. [19]Y. Lindell and B. Pinkas, “Privacy Preserving Data Mining,” J. Cryptology, vol. 15, no. 3, pp. 177-206, 2002. [20] Itai Ashlagi, Andrey Klinger, and Moshe Tennenholtz Technion–Israel, K-NCC: Stability against Group Deviations in Non-Cooperative Computation, 2007. [21] Y. Shoham and M. Tennenholtz, “Non-Cooperative Computation: Boolean Functions with Correctness and Exclusivity,” Theoretical Computer Science, vol. 343, nos. 1/2, pp. 97-113, 2005. [22] J. Vaidya and C. Clifton, “Privacy Preserving Association Rule Mining in Vertically Partitioned Data,” Proc. ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD ’02), pp. 639-644, July 2002
