Design and Implementation of a Smart Contract-Based Consent Management Model for Secure Personal Data Sharing - Mandinyenya, Godwin; Malele, Vusumuzi (Jurnal Ilmiah Computer Science, 2025)
Emerging data-sharing paradigms demand robust mechanisms to ensure user consent is dynamically managed while preserving data sovereignty. This paper proposes a blockchain-driven consent management model that leverages smart contracts, offline storage, and a JavaScript/JSON front end to empower data owners in healthcare, finance, and identity management. The framework decentralizes consent logging, automates access enforcement, and integrates GDPR-compliant "right to revoke" functionalities, addressing critical gaps in existing systems such as offline accessibility, cross-industry interoperability, and regulatory compliance. A mixed-methods approach—combining a systematic literature review (SLR) of 150 studies (2018–2023) and three case studies—validates the model's efficacy. Performance benchmarks reveal sub-second consent updates, 99.98% audit accuracy, and 40% reduced breach risks compared to centralized systems. The hybrid architecture employs a two-tiered design, with an on-chain layer for immutable consent logging and an offline layer for local data storage, ensuring enforceability even during network outages. The front end, built using React.js and Ethers.js, provides a user-friendly interface for non-technical users to define and manage consent terms. Security protocols, including FIDO2 authentication and AES-256-GCM encryption, ensure robust protection against unauthorized access. Challenges include gas cost volatility in public blockchains and latency in multi-chain consent synchronization. The study contributes a novel hybrid architecture, open-source front-end tools, and a regulatory alignment roadmap for decentralized consent ecosystems. Case studies in healthcare, finance, and identity management demonstrate the model's practical applicability, with unauthorized access reduced by 40% and user satisfaction scores exceeding 4.7/5. Future work will explore AI-driven consent drafting, interoperability standards, and quantum-resistant cryptography to further enhance the model's scalability and security. This research advances the state of the art in blockchain-based consent management, offering a scalable, secure, and user-centric solution for data sovereignty in the digital age.