Enhancing Traceability and Security in mHealth Systems: A Proximal Policy Optimization-Based Multi-Authority Attribute-Based Encryption Approach

Mobile healthcare (mHealth) is an emerging technology that facilitates the sharing of personal health records (PHR), but it also introduces risks related to the security and privacy of PHRs. To address these concerns, Attribute-Based Encryption (ABE) has been proposed as an advanced cryptographic solution to enable fine-grained access control over encrypted data. However, existing attribute-based mHealth systems often lack efficient traceability or are limited to a single authority. This paper presents a novel traceable multi-authority ABE scheme, incorporating Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning algorithm, to enhance traceability and security. The scheme is built on composite order groups and supports arbitrary monotonic access structures defined by Linear Secret Sharing Schemes (LSSS). The security of the system is validated under adaptive security assumptions, with PPO optimizing the access control decisions and identifying potential vulnerabilities in real-time. Additionally, the performance of the system is evaluated, demonstrating its efficiency, scalability, and availability for practical mHealth applications. This approach not only ensures robust access control but also guarantees high adaptability and traceability, effectively addressing privacy concerns in the sharing of sensitive health data.