Data Sanitization Based on Weaken_Sensitive_Pattern Tree

Abstract:

In data sharing, protecting the user's privacy data is very important. This article is based on data sanitization thought, and studies the protection of sensitive information. Then we proposed data sanitization algorithm (the Dpur algorithm), which is based on the weaken_sensitive_pattern tree. The results show that under the influencing of privacy protection factor, Dpur algorithm is superior to the SWA algorithm. When the shared data is the original data set, Dpur algorithm provides more efficient data sanitization.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 3167-3171
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.3167
• Online since: September 2013
• Authors: Xue Mei Jiang, Da Shen Xue
• Keywords: Candidate Items, Data Sanitization, Sensitive Items, Sensitive Pattern, Weaken_Sensitive_Pattern Tree

References

[1] D.E. Oleary. Knowledge discovery as a threat to database security. In: Proceedings of the 1st International Conference on Knowledge Discovery and Database, 1991: 507-516.

[2] R. Agrawal, A. Evfimievski, R. Srikant. Information sharing across private data-bases. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, 2003: 86-97.

DOI: 10.1145/872757.872771

[3] E. Dasseni, Vassilios S. Verykios, A. K. Elmagarmid et al. Hiding association rules by using confidence and support. In: Proceedings of the International Workshop on Information Hiding, 2001: 369-383.

DOI: 10.1007/3-540-45496-9_27

[4] Maurizio A, Francesco B, Fosca G et al. Blocking anonymity threats raised by frequent itemset mining. In: Proceedings of the 5th IEEE International Confenrence on Data Mining, 2005: 561-564.

DOI: 10.1109/icdm.2005.37

[5] D.E. Oleary. Knowledge discovery as a threat to database security. In: Proceedings of the 1st International Conference on Knowledge Discovery and Database, 1991: 507-516.

[6] R. Agrawal, A. Evfimievski, R. Srikant. Information sharing across private data-bases. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, 2003: 86-97.

DOI: 10.1145/872757.872771

[7] E. Dasseni, Vassilios S. Verykios, A. K. Elmagarmid et al. Hiding association rules by using confidence and support. In: Proceedings of the International Workshop on Information Hiding, 2001: 369-383.

DOI: 10.1007/3-540-45496-9_27

[8] Stanley R.M. Oliveira, Osmar R. Zaiane, Y. Saygin. Secure association rule sha-ring. In: Proceedings of the 8th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, 2010 (1): 106-110.

[9] Stanley R.M. Oliveira, Osmar R. Zaiane. Privacy Sensitive Knowledge by Data San-itization. In: Proceedings of the 3rd IEEE International Workshop on Secure Data Management in a Connected World, 2009: 67-82.

[10] H. Jiawei, H. Cheng, D Xin et al. Frequent pattern mining: current status and future directions. Data Mining Knowledge Discovery. 2007, 15: 55–86.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, p.68.

DOI: 10.1007/s10618-006-0059-1