Protecting Privacy Using K-Anonymity with a Hybrid Search Scheme

In this paper, a new search algorithm to achieve k-anonymity for protecting privacy is introduced. For this purpose, two algorithms, Tabu Search and Genetic Algorithm, are combined. The simulation results show that the proposed algorithm is superior to the individual search algorithm in average.

[1]  Rhonda Chaytor.  A Better Problem Representation for k-Anonymity , 2007 .

[2]  Pierangela Samarati,et al.  Protecting Respondents' Identities in Microdata Release , 2001, IEEE Trans. Knowl. Data Eng..

[3]  Jean-Pierre Corriveau,et al.  A globally optimal k-anonymity method for the de-identification of health data. , 2009, Journal of the American Medical Informatics Association : JAMIA.

[4]  Wang Zhi.  Clustering-Based Approach for Data Anonymization , 2010 .

[5]  David J. DeWitt,et al.  Mondrian Multidimensional K-Anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[6]  Roberto J. Bayardo,et al.  Data privacy through optimal k-anonymization , 2005, 21st International Conference on Data Engineering (ICDE'05).

[7]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[8]  Ran Wolff,et al.  The VLDB Journal manuscript No. (will be inserted by the editor) Providing k-Anonymity in Data Mining , 2022 .