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).
This work is funded by the Dutch national program COMMIT through the THeCS project and by the European Commission through the FP7 TClouds project (nr. 257243).
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Banescu, S., Petković, M., Zannone, N. (2012). Measuring Privacy Compliance Using Fitness Metrics. In: Barros, A., Gal, A., Kindler, E. (eds) Business Process Management. BPM 2012. Lecture Notes in Computer Science, vol 7481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32885-5_8
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DOI: https://doi.org/10.1007/978-3-642-32885-5_8
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