Uncertain Data Privacy Protection Based on K-Anonymity via Anatomy

Abstract:

In traditional database domain, k-anonymity is a hotspot in data publishing for privacy protection. In this paper, we study how to use k-anonymity in uncertain data set, use influence matrix of background knowledge to describe the influence degree of sensitive attribute produced by QI attributes and sensitive attribute itself, use BK(L,K)-clustering to present equivalent class with diversity, and a novel UDAK-anonymity model via anatomy is proposed for relational uncertain data. We will extend our ideas for handling how to solve privacy information leakage problem by using UDAK-anonymity algorithms in another paper.

• Periodical: Advanced Engineering Forum (Volumes 6-7)
• Pages: 64-69
• DOI: https://doi.org/10.4028/www.scientific.net/AEF.6-7.64
• Online since: September 2012
• Authors: Xiang Min Ren, Jing Yang, Jian Pei Zhang, Zong Fu Jia
• Keywords: K-Anonymity, Privacy Protection, UADK-Anonymity, Uncertain Data

References

[1] ZHOU Aoying et al. A Survey on the M anagement of Uncertain Data, Chinese journal of Computers. 32(2009) 1-15.

[2] Samarati P, Sweeney L, Generalizing data to provide anonymity when disclosing information, Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS, (1998) 188.

DOI: 10.1145/275487.275508

[3] Aggarwal Charu. C, On Unifying Privacy and Uncertain Data Models, Proceedings of the 2008 IEEE 24th International Conference on Data Engineering. (2008) 386-395.

DOI: 10.1109/icde.2008.4497447

[4] Wu Jia-wei, Liu Guo-hua, Wang Mei, Modeling the Uncertain data in data in the K-anonymity Privacy Protection Mode., CPMPUTER EINGINEERING & SCIENCE. 33(2011) 7-13.

[5] L. Sweeney, k-anonymity: a model for protecting privacy, International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (2002) 557-570.

DOI: 10.1142/s0218488502001648

[6] L. Sweeney, Achieving k-anonymity privacy protection using generalization and suppression, International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (2002) 571-588.

DOI: 10.1142/s021848850200165x

[7] Xiangmin, Ren, et al., Research on CBK(L, K)-anonymity algorithm, International Journal of Advancements in Computing Technology. 3(2011) 165-173.

[8] LI Tai-yong, et al., k-Anonymity via Twice Clustering for Privacy Preservation, Journal of Jilin University. 27(2009) 173-178.

[9] XiaoXiaokui, TaoYufei, Anatomy: Sample and effective protect preservation, Proceedings of the 32nd International Conference on VeryLarge DataBases. (2006)139-150.

[10] Xiaoxun Sun, Hua Wang, Jiuyong Li, David Ross, Achieving p-Sensitive k-Anonymity via Anatomy, Proceedings of the 2009 IEEE International Conference on e-Business Engineering. (2009) 199-205.

DOI: 10.1109/icebe.2009.34

[11] Anish Das Sarma, Shubha U. Nabar, Jennifer Widom, Representing Uncertain Data: Uniqueness, Equivalence, Minimization, and Approximation. ．Technical Report, Stanford Infolab (2005).

[12] Anish Das Sarma, Omar Benjelloun, Alon Halevy, Shubha Nabar, JenniferWidom, Representing Uncertain Data: Models, Properties, and Algorithms. The VLDB Joural. 18(2009) 989-1019.

DOI: 10.1007/s00778-009-0147-0

[13] Ren Xiangmin, Yang Jing, Zhang Jianpei, Wang Kechao, Research on CBK(L, K)-Anonymity Algorithm , International Journal of Advancements in Computing Technology. 3(2011) 165 -173.