CFP last date
20 January 2025
Reseach Article

Third Party Privacy Preserving Protocol for Secure Web Services

by B. Raghuram, S. Sandeep, B. Hanmanthu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 82 - Number 3
Year of Publication: 2013
Authors: B. Raghuram, S. Sandeep, B. Hanmanthu
10.5120/14099-2118

B. Raghuram, S. Sandeep, B. Hanmanthu . Third Party Privacy Preserving Protocol for Secure Web Services. International Journal of Computer Applications. 82, 3 ( November 2013), 32-35. DOI=10.5120/14099-2118

@article{ 10.5120/14099-2118,
author = { B. Raghuram, S. Sandeep, B. Hanmanthu },
title = { Third Party Privacy Preserving Protocol for Secure Web Services },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 82 },
number = { 3 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 32-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume82/number3/14099-2118/ },
doi = { 10.5120/14099-2118 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:50.401960+05:30
%A B. Raghuram
%A S. Sandeep
%A B. Hanmanthu
%T Third Party Privacy Preserving Protocol for Secure Web Services
%J International Journal of Computer Applications
%@ 0975-8887
%V 82
%N 3
%P 32-35
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Web services is become major issue in distributed data mining. In the literature we can found a number of proposals of privacy preserving which can be divided into two major categories that is trusted third party and multiparty based privacy protocols. In case of the trusted third party privacy protocol models the conventional asymmetric cryptographic based techniques or algorithms will be used and in case of the multi party based protocols data perturbed technique can be used to ensure no other party to understand original data. In order to improve security features by combining strengths of both the above said models in this work, we propose to use data perturbed techniques in third party privacy preserving protocol to conduct the secure web service. In order to perform web service we propose third party protocol for secure computations. Our results shown that although the data are disguised and decentralized, our method can still achieve literally high accuracy.

References
  1. M Zaki. Parallel and Distributed Data Mining: An Introduction. In Large-Scale Parallel Data Mining, pages 1–23. 2000.
  2. J. Vaidya, and C. Clifiton, "Privacy Preserving Association Rule Mining in Vertically Partitioned Data," Proceedings of SIGKDD, Canada, 2002.
  3. R. Agrawal and R. Srikant. Privacy-preserving data mining. In SIGMOD Conference, pages 439–450, 2000.
  4. D. Agrawal and C. C. Aggarwal. On the design and quantification of privacy preserving data mining algorithms. In PODS. ACM, 2001.
  5. H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar. On the privacy preserving properties of random data perturbation techniques. In ICDM, pages 99–106. IEEE Computer Society, 2003.
  6. W. Du and Z. Zhan. Using randomized response techniques for privacy-preserving data mining. In KDD, pages 505– 510, 2003.
  7. A. V. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke. Privacy preserving mining of association rules. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 217–228,2002.
  8. M. Kantarcioglu and C. Clifton. Privately computing a distributed k-nn classifier. In J. -F. Boulicaut, F. Esposito, F. Giannotti, and D. Pedreschi, editors, PKDD, volume 3202 of Lecture Notes in Computer Science, pages 279–290. Springer, 2004.
  9. Y. Lindell and B. Pinkas. Privacy preserving data mining. In M. Bellare, editor, CRYPTO, volume 1880 of Lecture Notes in Computer Science, pages 36–54. Springer, 2000.
  10. L. Liu, M. Kantarcioglu, and B. Thuraisingham. The applicability of the perturbation based privacy preserving data mining for real-world data. Data and Knowledge Engineering Journal, 2007.
  11. T. M. Mitchell. Machine Learning. mcgraw-hill, 1997.
  12. B. Rozeberg, and E. Gudes "Association rule mining in vertically partioned databases", Data and Knowledge Engineering, Elsever, pp378-396,2006.
  13. Z. Yang,and N. Wright "Privacy-Preserving Computation of Bayesian Networks on Vertically Partitioned Data", IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 9, pp 1253-1265, 2006.
  14. H. Zheng, and S. R. Kulkarni "Attribute distributed learning: Models,limits, and aglorithms" . IEEE Transactions on Signal processing, Vol. 59,no. 1, pp386-398,2011.
  15. Y. Sang, H. Shen,H. Tain, "Privacy preserving tuple matching in distributed databases", IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 12, pp 1767-1782, 2009.
  16. A. Delis and V. S. Verykos, and A. Tisonsis, "Data pertubaration approach to sensitive classification rule hiding,"Prociding of ACM SAC 10, New York,USA, pp. 605-609, 2010.
  17. B. Hanmanthu, B. RaghuRam, and P. Niranjan, "Third Party Privacy Preserving Protocol for Perturbation Based Classification of Vertically Fragmented Databases," in Proceedings of ELSEVIER, International Conference on Emerging Trends in Electrical, Communication and Information Technologies (ICECIT - 2012), 21-23 December 2012, Anantapur - 515 701, Andhra Pradesh, India. pp. 109-113.
  18. B RaghuRam, Jayadev Gyani, B. Hanmanthu "Fuzzy Associative Classifier for Distributed Mining," in Proceedings of IJCA, International Conference and workshop on Emerging Trends in Technology (ICWET 2012),24-25 February 2012, Mumbai,India,pp. 431-435. (ISBN:973-93-80864-47-3)
Index Terms

Computer Science
Information Sciences

Keywords

Distributed Data mining Vertical Fragmentation Third Party Privacy Preserving Data Perturbation Web Service