Measuring Privacy Compliance Using Fitness Metrics

Abstract

Nowadays, repurposing of personal data is a major privacy issue. Detection of data repurposing requires posteriori mechanisms able to determine how data have been processed. However, current a posteriori solutions for privacy compliance are often manual, leading infringements to remain undetected. In this paper, we propose a privacy compliance technique for detecting privacy infringements and measuring their severity. The approach quantifies infringements by considering a number of deviations from specifications (i.e., insertion, suppression, replacement, and re-ordering).

DOI: 10.1007/978-3-642-32885-5_8

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Cite this paper

@inproceedings{Banescu2012MeasuringPC, title={Measuring Privacy Compliance Using Fitness Metrics}, author={Sebastian Banescu and Milan Petkovic and Nicola Zannone}, booktitle={BPM}, year={2012} }