Khalid, Muhammad Irfan, Ahmed, Mansoor and Kim, Jungsuk (2023) Enhancing Data Protection in Dynamic Consent Management Systems: Formalizing Privacy and Security Definitions with Differential Privacy, Decentralization, and Zero-Knowledge Proofs. Sensors, 23 (17). p. 7604. ISSN 1424-8220
Preview
AhmedMansoorZero2023.pdf
Download (9MB) | Preview
Abstract
Dynamic consent management allows a data subject to dynamically govern her consent
to access her data. Clearly, security and privacy guarantees are vital for the adoption of dynamic
consent management systems. In particular, specific data protection guarantees can be required
to comply with rules and laws (e.g., the General Data Protection Regulation (GDPR)). Since the
primary instantiation of the dynamic consent management systems in the existing literature is
towards developing sustainable e-healthcare services, in this paper, we study data protection issues
in dynamic consent management systems, identifying crucial security and privacy properties and
discussing severe limitations of systems described in the state of the art. We have presented the
precise definitions of security and privacy properties that are essential to confirm the robustness of
the dynamic consent management systems against diverse adversaries. Finally, under those precise
formal definitions of security and privacy, we have proposed the implications of state-of-the-art tools
and technologies such as differential privacy, blockchain technologies, zero-knowledge proofs, and
cryptographic procedures that can be used to build dynamic consent management systems that are
secure and private by design.
Item Type: | Article |
---|---|
Keywords: | data protection; dynamic consent management; security; privacy by design; general data protection regulation (GDPR); differential privacy; blockchain; |
Academic Unit: | Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI |
Item ID: | 18910 |
Identification Number: | 10.3390/s23177604 |
Depositing User: | IR Editor |
Date Deposited: | 19 Sep 2024 14:57 |
Journal or Publication Title: | Sensors |
Publisher: | MDPI AG |
Refereed: | Yes |
Related URLs: | |
URI: | https://mu.eprints-hosting.org/id/eprint/18910 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
Downloads
Downloads per month over past year