Evaluation of generalization based K-anonymization algorithms

The Electronic-Era has brought the major challenge to the individual's privacy by collecting the individual's information. This information is a threat to the privacy as it is published to the third party for the purpose of either research or study. Even though the identity is not published, based on some informative attributes and publicly available data, fraudulent can access the information which is supposed to be private. As a result, many researchers are attracted towards the challenge and developed many solutions. This paper is aimed to give comparative evolution of the various generalization hierarchy based K-anonymization algorithms. Major challenge while preserving the privacy of an individual, is to keep published data useful for the further research and analysis. Also, the data generated is voluminous and it should take less amount of time for anonymization. In this work these algorithms are compared for efficiency (in terms of time) and utility loss.

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

[2]  Fabian Prasser,et al.  ARX - A Comprehensive Tool for Anonymizing Biomedical Data , 2014, AMIA.

[3]  Latanya Sweeney,et al.  Achieving k-Anonymity Privacy Protection Using Generalization and Suppression , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[4]  Pierangela Samarati,et al.  Generalizing Data to Provide Anonymity when Disclosing Information , 1998, PODS 1998.

[5]  Sabrina De Capitani di Vimercati,et al.  Data Privacy: Definitions and Techniques , 2012, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[6]  Thomas Cerqueus,et al.  A Systematic Comparison and Evaluation of k-Anonymization Algorithms for Practitioners , 2014, Trans. Data Priv..

[7]  Jimeng Sun,et al.  Publishing data from electronic health records while preserving privacy: A survey of algorithms , 2014, J. Biomed. Informatics.

[8]  Nitesh Kumar,et al.  Achieving k-anonymity Using Improved Greedy Heuristics for Very Large Relational Databases , 2013, Trans. Data Priv..

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

[10]  Ken Mano,et al.  Anonymity, Privacy, Onymity, and Identity: A Modal Logic Approach , 2009, 2009 International Conference on Computational Science and Engineering.

[11]  Vijay S. Iyengar,et al.  Transforming data to satisfy privacy constraints , 2002, KDD.

[12]  Claudia Eckert,et al.  Flash: Efficient, Stable and Optimal K-Anonymity , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[13]  David J. DeWitt,et al.  Incognito: efficient full-domain K-anonymity , 2005, SIGMOD '05.

[14]  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.