Organisations are coming under increasing pressure to respect and protect personal data privacy, especially with the European Union’s General Data Protection Regulation (GDPR) now in effect. As legislation and regulation evolve to incentivise such data-handling protection, so too does the business case for demonstrating compliance both in spirit and to the letter. Compliance will require ongoing checks as modern systems are constantly changing in terms of digital infrastructure services and business offerings, and the interaction between human and machine. Therefore, monitoring for compliance during run-time is likely to be required. There has been limited research into how to monitor how well a system respects consents given, and withheld, pertaining to handling and onward sharing. This paper proposes a finite-state-machine method for detecting violations of preferences (consents and revocations) expressed by Data Subjects regarding use of their personal data, and also violations of any related obligations that might be placed upon data handlers (data controllers and processors). Our approach seeks to enable detection of both accidental and malicious compromises of privacy properties. We also present a concept demonstrator to show the feasibility of our approach and discuss its design and technical implementation.
CITATION STYLE
Happa, J., Moffat, N., Goldsmith, M., & Creese, S. (2019). Run-time monitoring of data-handling violations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11387 LNCS, pp. 213–232). Springer Verlag. https://doi.org/10.1007/978-3-030-12786-2_13
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