Anonoymizing Methods against Republication of Incremental Numerical Sensitive Data

Abstract:

Privacy protection for numerical sensitive data has become a serious concerned in many applications. Current privacy protection for numerical sensitive data base on the static datasets. However, most of the real world data sources are dynamic, and the direct application of the existing static datasets privacy preserving techniques often causes the unexpected private information disclosure. This paper anaylisis various leakage risks of republication of incremental numerical sensitive data on numerical sensitive data, and proposes an efficient algorithm on anonoymizing methods against republication of incremental numerical sensitive data,The experiments show that this method protects privacy adequately.

• Periodical: Advanced Materials Research (Volume 267)
• Pages: 499-503
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.267.499
• Online since: June 2011
• Authors: Xiao Lin Zhang, Jie Yu, Yue Sheng Tan, Li Xin Liu
• Keywords: Incremental Data, Numerical Sensitive Data, Privacy Protection

References

[1] L . Sweeney. K-anonymity: A model for protecting privacy. International Journal on Uncertainty, Fuzziness, and Knowledge-based Systems, Singapore , World Scientific Publishing , 2002, pp.571-588.

[2] L. Sweeney. Achieving k-anonymity privacy protection using generalization and suppression . International Journal on Uncertainty, Fuzziness, and Knowledge-based Systems, Singapore, World Scientific Publishing, 2002, p.571–588.

DOI: 10.1142/s021848850200165x

[3] A. Machanavajjhala ,J. Gehrke, and D. Kifer. l-diversity: privacy beyond k-anonymity. In Proc. of International Conference on Data Engineering, Piscataway , Computer Society, 2006, pp.24-36.

DOI: 10.1109/icde.2006.1

[4] X. Xlao, Y. Tao. m-Invarianee: towards Privacy Preserving re–publication of dynamic datasets. In Proc. Of the ACM SIGMOD Conference on Management of Data, New York , Association for Computing Machinery , 2007, pp.689-700.

DOI: 10.1145/1247480.1247556

[5] K. LeFevre, D.J. Dewitt, and R. Ramakrishnan. Workload-aware anonymization. In Proc. of ACM Knowledge Discovery and Data Mining, New York, Association for Computing Machinery, 2006, pp.277-286.

DOI: 10.1145/1150402.1150435

[6] Q. Zhang, N. Koudas, D. Srivastava, and T. Yu. Aggregate query answering on anonymized tables. In Proc. of International Conference on Data Engineering, Piscataway, Computer Society, 2007, pp.116-125.

DOI: 10.1109/icde.2007.367857

[7] N. Li, T. Li, and S. Venkatasubramanian. t-closeness: Privacy beyond k-anonymity and l-diversity. In Pro. of the International Conference on Data Engineering, Piscataway, Computer Society 2007, pp.106-115.

DOI: 10.1109/icde.2007.367856

[8] Jiexing. Li, Yu. fei. Tao, and Xiaokui. Xiao. Preservation of Proximity Privacy in Publishing Numerical Sensitive Data. In Proc. of ACM Knowledge Discovery and Data Mining, New York, Association for Computing Machinery, 2008, pp.473-486.

DOI: 10.1145/1376616.1376666

[9] P. Samarati and L. Sweeney. Generalizing data to provide anonymity when disclosing information. In Pro. of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database, New York, ACM, 1998, p.188.

DOI: 10.1145/275487.275508

[10] J. Pei, J. Xu, Z. Wang, W. wang, and K. Wang, Maintaining k-anonymity against incremental updates. In Proc. of the International Conference on Scientific and Statistical Database Management , 2007, pp.1-10.

DOI: 10.1109/ssdbm.2007.16

[11] R. Floyd and R. Rivest. Expected time bounds for selection. In Communications of the ACM (CACM) Vol. 18(1975), pp.165-172.

DOI: 10.1145/360680.360691