Thoughts on k-anonymization |
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| Affiliation: | 1. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran;2. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746 Tehran, Iran;3. Department of Electrical and Computer Engineering, University of Puerto Rico at Mayaguez, Mayaguez, PR, USA;1. Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan;2. JST, PRESTO, Kawaguchi, Saitama, Japan;1. Center of Information Technology, Ministry of Agriculture, China;2. School of Computer Science, University of Adelaide, Australia;3. School of Data and Computer Science, Sun Yat-Sen University, China |
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| Abstract: | k-Anonymity is a method for providing privacy protection by ensuring that data cannot be traced to an individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. To achieve optimal and practical k-anonymity, recently, many different kinds of algorithms with various assumptions and restrictions have been proposed with different metrics to measure quality. This paper evaluates a family of clustering-based algorithms that are more flexible and even attempts to improve precision by ignoring the restrictions of user-defined Domain Generalization Hierarchies. The evaluation of the new approaches with respect to cost metrics shows that metrics may behave differently with different algorithms and may not correlate with some applications’ accuracy on output data. |
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