Attribute-Based Encryption with Secure Multi-Party Computation for Fine-Grained Access Control in Cloud-Based Healthcare Systems.

Cloud-based healthcare systems have transformed the management and sharing of electronic health records (EHRs), telemedicine data, and collaborative medical research by offering scalability, cost efficiency, and real time accessibility. However, this transformation exposes patient data to risks such as breaches, insider threats, and unauthorized disclosures. Traditional access control mechanisms like Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Identity-Based Encryption (IBE) prove insufficient in dynamic, multi-stakeholder healthcare environments. This research proposes a hybrid framework integrating Attribute Based Encryption (ABE) for fine-grained, policy-driven confidentiality and Secure Multi-Party Computation (SMC) for privacy-preserving collaborative analytics. The framework ensures that sensitive health data remains protected while enabling secure computations across distributed institutions. ABE enforces patient- and context centric access policies, while SMC enables multi-institutional analytics without exposing raw records. The proposed system is evaluated through security analysis and performance benchmarks, highlighting trade-offs between encryption costs, ciphertext expansion, computation overheads, and communication latency. The results demonstrate that ABE + SMC integration can achieve confidentiality, collusion resistance, and regulatory compliance (HIPAA/GDPR), while supporting practical applications such as multi-hospital predictive analytics, genomics, and clinical trials. Despite challenges in key management, revocation, and computational scalability, this hybrid model represents a paradigm shift toward secure, collaborative, and patient-centric healthcare ecosystems.