Formal modeling and analysis of data protection for GDPR compliance of IoT healthcare systems

Kammueller, Florian ORCID logoORCID: https://orcid.org/0000-0001-5839-5488 (2018) Formal modeling and analysis of data protection for GDPR compliance of IoT healthcare systems. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). In: IEEE SMC 2018: IEEE International Conference on Systems, Man and Cybernetics, 08-10 Oct 2018, Miyazaki, Japan. . [Conference or Workshop Item]

[img]
Preview
PDF - Final accepted version (with author's formatting)
Download (469kB) | Preview

Abstract

In this paper, we investigate the implications of the General Data Privacy Regulation (GDPR) on the design of an IoT healthcare system. From 26th May 2018, the GDPR will become mandatory within the European Union and hence also for any supplier of IT products. Breaches of the regulation will be fined with penalties of 20 Million EUR. This is a strong motivation for system designers to enable the proof of compliance to the GDPR. We propose the use of formal modeling and analysis using interactive theorem proving. Based on previous work on modeling infrastructures and security policies for insider attacks, we demonstrate the use of logical modeling and machine assisted verification to support data protection (privacy) by design. We illustrate this process on the case study of IoT based monitoring of Alzheimer’s patients that we work on in the CHIST-ERA project SUCCESS.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 25282
Notes on copyright: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Useful Links:
Depositing User: Florian Kammueller
Date Deposited: 03 Oct 2018 11:22
Last Modified: 07 Jun 2022 21:59
URI: https://eprints.mdx.ac.uk/id/eprint/25282

Actions (login required)

View Item View Item

Statistics

Activity Overview
6 month trend
449Downloads
6 month trend
324Hits

Additional statistics are available via IRStats2.