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Measuring Privacy Compliance Using Fitness Metrics

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Business Process Management (BPM 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7481))

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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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References

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© 2012 Springer-Verlag Berlin Heidelberg

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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32884-8

  • Online ISBN: 978-3-642-32885-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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