Maximum Entropy Oriented Anonymization Algorithm for Privacy Preserving Data Mining

• Published in: Global Security, Safety and Sustainability & e-Democracy Vol. 99; pp. 9 - 16
• Main Authors: Tsiafoulis, Stergios G., Zorkadis, Vasilios C., Pimenidis, Elias
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2012; Springer Berlin Heidelberg
• Series: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
• Subjects: anonymity; closeness; maximum entropy; maximum entropy anonymity; neural-network clustering; Privacy preservation; SOMs; ℓ-diversity
• ISBN: 9783642334474; 3642334474
• ISSN: 1867-8211; 1867-822X
• DOI: 10.1007/978-3-642-33448-1_2

This work introduces a new concept that addresses the problem of preserving privacy when anonymising and publishing personal data collections. In particular, a maximum entropy oriented algorithm to protect sensitive data is proposed. As opposed to k-anonymity, ℓ-diversity and t-closeness, the proposed algorithm builds equivalence classes with possibly uniformly distributed sensitive attribute values, probably by means of noise, and having as a lower limit the entropy of the distribution of the initial data collection, so that background information cannot be exploited to successfully attack the privacy of data subjects data refer to. Furthermore, existing privacy and information loss related metrics are presented, as well as the algorithm implementing the maximum entropy anonymity concept. From a privacy protection perspective, the achieved results are very promising, while the suffered information loss is limited.