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Mathematical Reconciliation of Medical Privacy Policies

Published: 08 December 2020 Publication History

Abstract

Healthcare data are arguably the most private of personal data. This very private information in the wrong hands can lead to identity theft, prescription fraud, insurance fraud, and an array of other crimes. Electronic-health systems such as My Health Record in Australia holds great promise in sharing medical data and improving healthcare quality. But, a key privacy issue in these systems is the misuse of healthcare data by “authorities.” The recent General Data Protection Regulation (GDPR) introduced in the EU aims to reduce personal-data misuse. But, there are no tools currently available to accurately reconcile a domestic E-health policy against the GDPR to identify discrepancies. Reconciling privacy policies is also non-trivial, because policies are often written in free text, making them subject to human interpretation.
In this article, we propose a tool that allows the description of E-health privacy policies, represents them using formal constructs making the policies precise and explicit. Using this formal framework, our tool can automatically reconcile a domestic E-health policy against the GDPR to identify violations and omissions. We use our prototype to illustrate several critical flaws in Australia’s My Health Record policy, including a non-compliance with GDPR that allows healthcare providers to access medical records by default.

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      cover image ACM Transactions on Management Information Systems
      ACM Transactions on Management Information Systems  Volume 12, Issue 1
      Special Issue on Analytics for Cybersecurity and Privacy, Part 2 and Regular Papers
      March 2021
      174 pages
      ISSN:2158-656X
      EISSN:2158-6578
      DOI:10.1145/3441846
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 08 December 2020
      Online AM: 07 May 2020
      Accepted: 01 April 2020
      Revised: 01 March 2020
      Received: 01 September 2019
      Published in TMIS Volume 12, Issue 1

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      Author Tags

      1. GDPR
      2. metagraph
      3. my health record
      4. privacy policy

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      • The ARC Center of Excellence for Mathematical and Statistical Frontiers

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