Extended K-Anonymity Model for Privacy Preserving on Micro Data

Автор: Masoud Rahimi, Mehdi Bateni, Hosein Mohammadinejad

Журнал: International Journal of Computer Network and Information Security(IJCNIS) @ijcnis

Статья в выпуске: 12 vol.7, 2015 года.

Бесплатный доступ

Today, information collectors, particularly statistical organizations, are faced with two conflicting issues. On one hand, according to their natural responsibilities and the increasing demand for the collected data, they are committed to propagate the information more extensively and with higher quality and on the other hand, due to the public concern about the privacy of personal information and the legal responsibility of these organizations in protecting the private information of their users, they should guarantee that while providing all the information to the population, the privacy is reasonably preserved. This issue becomes more crucial when the datasets published by data mining methods are at risk of attribute and identity disclosure attacks. In order to overcome this problem, several approaches, called p-sensitive k-anonymity, p+-sensitive k-anonymity, and (p, α)-sensitive k-anonymity, were proposed. The drawbacks of these methods include the inability to protect micro datasets against attribute disclosure and the high value of the distortion ratio. In order to eliminate these drawbacks, this paper proposes an algorithm that fully protects the propagated micro data against identity and attribute disclosure and significantly reduces the distortion ratio during the anonymity process.

Еще

Privacy preservation, data mining, k-anonymity, micro data

Короткий адрес: https://sciup.org/15011481

IDR: 15011481

Список литературы Extended K-Anonymity Model for Privacy Preserving on Micro Data

  • L. Willenborg and T.Waal "Elements of Statistical Disclosure Control", Springer, 2001.
  • S. Morton, "An Improved Utility Driven Approach Towards k-anonymity Using Data Constraint Rules", Submitted to the faculty of the University Graduate School in partial fulfillment of the requirements for the degree Doctor of Philosophy in the School of Informatics, Indiana University , 2012.
  • X. Qi, and M. Zong, "An Overview of Privacy Preserving Data Mining", International Conference on Environmental Science and Engineering, ICESE, p. 1341-1347, 2012.
  • D. Agrawal, and C.C. Aggarwal, "On the design and quantification of privacy preserving data mining algorithms", Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Volume NY, pp. 247-255, 2001.
  • L. Sweeney, "k-anonymity: A model for protecting privacy," International Journal of Uncertainty Fuzziness and Knowledge Based Systems", vol. 10, no. 5, pp. 557-570, 2002.
  • J. Yongcheng and S.Jiajin Le, "A Survey on Anonymity-based Privacy Preserving", Proceedings of the E-Business and Information System Security, p. 1 - 4, 2009.
  • A. Meyerson, and R. Williams, "On the complexity of optimal k-anonymity", Proceedings of the 23rd ACM-SIGMOD-SIGACT-SIGART Symposium on the Principles of Database Systems, Paris, France, pp. 223-228, 2004.
  • G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, and A. Zhu, "Anonymizing tables", Proceedings of the 10th International Conference on Database Theory (ICDT'05), Edinburgh, Scotland, pp. 246-258, 2006.
  • X. Sun, L. Sun, and H. Wang, "Extended k-anonymity models against sensitive attribute disclosure", Computer Communications, Volume 34, Issue 4, Pages 526–535, 2011.
  • G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, and A. Zhu, "Approximation algorithms for k-anonymity", Proceedings of the 2007 ACM SIGMOD international conference on Management of data, p. 67-78, 2007.
  • C. Moque, A. Pomares, and R. Gonzalez, "AnonymousData.co: A proposal for Interactive Anonymization of Electronic Medical Records", Proceedings of the 4th Conference of ENTERprise Information Systems-aligning technology, 2012.
  • A. Machanavajjhala, J.Gehrke, and D. Kifer, "L-diversity: Privacy beyond k-anonymity", Proceedings of the ICDE", p.24, 2006.
  • N. Li, T. Li, and S. Venkatasubramanian, "t-Closeness: Privacy Beyond k-anonymity and L-Diversity", Proceedings of the ICDE, pp.106-115, 2007.
  • T. Waal, and L. Willenborg, "Information loss through global recoding and local suppression", Proceedings of the Netherlands Official Statistics, Volume 14, p. 17-20, spring 1999.
  • P. Wolf, J. Gouweleeuw, P. Kooiman, and L. Willenborg, "Reflections on PRAM", Proceedings of the Statistics Netherlands Department of Statistical Methods 1998.
  • C. Chang, Y. Li, and W. Huang, "TFRP: An efficient micro aggregation algorithm for statistical Disclosure control", The Journal of Systems and Software, Volume 80, Issue 11, p.1866-1878, 2007.
  • Z. FeiFei, D. LiFeng, W. Kun, and L. Yang, "Study on Privacy Protection Algorithm Based on K-Anonymity", International Conference on Medical Physics and Biomedical Engineering, Volume 33, p. 483 - 490, 2012.
  • G. Torra, A. Erola, and J. Roca,"User k-anonymity for privacy preserving data mining of query logs", Information Processing and Management, Volume 48, Issue 3, p.476-487, 2012.
  • C. Tai, P. Yu, and M. Chen," k-Support Anonymity Based on Pseudo Taxonomy for Outsourcing of Frequent Item set Mining", KDD'10 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pages 473-482, 2010.
  • K. Doka, D. Tsoumakos, and N. Koziris," KANIS: Preserving k-anonymity Over Distributed Data", Research Center Athena, Athens, Greece, 2011.
  • D.J. Newman, S. Hettich, C.L. Blake, C.J. Merz, UCI Repository of Machine Learning Databases. Available at
  • D. Pelleg and A. Moore: "X-Means: Extending K-Means with Efficient Estimation of the number clusters", in: ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning, pp 727-734, 2000.
Еще
Статья научная