The Analysis of Typical Algorithms Based on K-Anonymity

Abstract:

The backgrounds that K-anonymity was put forward are described. K-anonymity, Quasi Identifier and other related concepts are given. We also analyse the basic algorithms based on K-anonymity such as Datafly algorithm, MinGen (Minimal Generalization) algorithm, Incognito algorithm, Classfly & Classfly+ algorithm, Multiple Constraints algorithm etc, and point out the advantages and flaws of these algorithms. Finally, the future directions in the field are discussed.

• Periodical: Applied Mechanics and Materials (Volumes 411-414)
• Pages: 327-330
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.411-414.327
• Online since: September 2013
• Authors: Ti Gui Fan
• Keywords: Algorithm (GA), K-Anonymity, Privacy Protection, Quasi Identifier

References

[1] Samarati P, Sweeney L. Generalizing data to provide anonymity when disclosing information (Abstract) [C]/Proc of the 17th ACMSIGMODSIGACT- SIGART Symposium on the Principles of Database Systems, Seattle, WA, USA (1998), p.188.

DOI: 10.1145/275487.275508

[2] Chen Ting-ting, Han Jian-min, Wang ji-yi, Li Xi-yu. Survey of K-anonymity reseach on privacy preservation. Computer Engineering and Applications, Vol. 44 (2008), pp.130-134.

[3] Qin Xiaowei, Men Aihua. The research on privacy protection based on K-anonmity. Journal of Chifeng University, Vol. 26 (2010), pp.14-16.

[4] Sweeney L. Computational Disclosure Control: Aprimer on data privacy protection. [Ph.D. Thesis, Massachusetts Institute of Technology], (2001), pp.67-82.

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

DOI: 10.1142/s021848850200165x

[6] LeFevre K, DeWitt D, Ramakrishnan R. Incognito: EfficientFull-domain k-anonymity [Z]. In Proc. Of theACM SIGMOD Int'l Conf on Management of Data, Baltimore, Maryland, USA, (2005), pp.49-60.

DOI: 10.1145/1066157.1066164

[7] Liu XY, Yang XC, Yu G. A representative class basedprivacy preserving data publishing approach with highprecision. Computer Science, Vol. 32 (2005), pp.368-373.

[8] Yang XC, Liu XY, Wang Bin. K-Anonymity Approaches for Supporting Multiple Constraints. Journal of Software, Vol. 17 (2006), pp.1222-1231.

[9] LeFevre K, DeWitt D, Ramakrishnan R. Mondrianmultidimensional K -anonymity [C]. Proc of 22ndICDE. Los Alamitos, USA: IEEE Computer SocietyPress, (2006), pp.25-34.

[10] Yan Hua, Liu Gui-song. Multidimensional K-anonymity Partition Method Using Entropy. Journal of University of Electronic Science and Technology of China, Vol. 36 (2007), pp.1228-1231.