Measuring privacy compliance using fitness metrics

Authors:
Sebastian Banescu;Milan Petković;Nicola Zannone
Affiliations:
Philips Research Eindhoven, The Netherlands,Eindhoven University of Technology, The Netherlands;Philips Research Eindhoven, The Netherlands,Eindhoven University of Technology, The Netherlands;Eindhoven University of Technology, The Netherlands
Venue:
BPM'12 Proceedings of the 10th international conference on Business Process Management
Year:
2012

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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).