A Post-processing Method to Lessen k-Anonymity Dissimilarities

Protecting personal data is essential to guarantee the rule of law 1 . Due to the new Information and Communication Technologies (ICTs) unprecedented amounts of personal data can be stored and analysed. Thus, if the proper measures are not taken, individual privacy could be in jeopardy. Being the ai...

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Bibliographic Details
Main Authors: Solanas, A., Pujol, G., Martinez-Balleste, A., Mateo-Sanz, J.M.
Format: Conference Proceeding
Language:English
Subjects:
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Communications technology
Computer science
Data privacy
Data security
Information analysis
k-anonymity
Mathematics
micro-aggreagtion
Privacy
Protection
Radiofrequency identification
Security
Statistics
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Summary:Protecting personal data is essential to guarantee the rule of law 1 . Due to the new Information and Communication Technologies (ICTs) unprecedented amounts of personal data can be stored and analysed. Thus, if the proper measures are not taken, individual privacy could be in jeopardy. Being the aim to protect individual privacy, a great variety of statistical disclosure control (SDC) techniques has been proposed. Amongst many others, k-anonymity is a promising property that, if properly achieved, can help protect individual privacy. In this paper, we propose a new post-processing method that can be applied after a k-anonymity algorithm, being the aim to lessen the errors resulting from the aggregation of data. We show that our method can be extended to work with many other SDC techniques and we provide some experimental results which emphasise the usefulness of our proposal.
DOI:10.1109/ARES.2008.93