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I Did Not Accept That: Demonstrating Consent in Online Collection of Personal Data

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11711))

Abstract

Privacy in online collection of personal data is currently a much debated topic considering, amongst other reasons, the incidents with well known digital organisations, such as social networks and, in Europe, the recent EU/GDPR regulation. Among other required practices, explicit and simply worded consent from individuals must be obtained before collecting and using personal information. Further, individuals must also be given detailed information about what, how and what for data is collected. Consent is typically obtained at the collection point and, at a single point in time (ignoring updates), associated with Privacy Policies or End-User Agreements. At any moment, both the user and the organization should be able to produce evidence of this consent. This proof should not be disputable which leads us to strong cryptographic properties.

The problem we discuss is how to robustly demonstrate such consent was given. We adapt fair-exchange protocols to this particular problem and, upon an exchange of personal data, we are able to produce a cryptographic receipt of acceptance that any party can use to prove consent and elicit non-repudiation. We discuss two broad strategies: a pure peer-to-peer scheme and the use of a Trusted Third Party.

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Notes

  1. 1.

    We are working with Privacy organisations towards piloting the ideas on this paper in a real-world scenario.

  2. 2.

    See https://tldrlegal.com/ for software licenses.

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Acknowledgments

We would like to thank Professor Peter Sommer, of Birmingham City University, for his insights into the legal aspects.

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Correspondence to Vitor Jesus .

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Jesus, V., Mustare, S. (2019). I Did Not Accept That: Demonstrating Consent in Online Collection of Personal Data. In: Gritzalis, S., Weippl, E., Katsikas, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Trust, Privacy and Security in Digital Business. TrustBus 2019. Lecture Notes in Computer Science(), vol 11711. Springer, Cham. https://doi.org/10.1007/978-3-030-27813-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-27813-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27812-0

  • Online ISBN: 978-3-030-27813-7

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