Enhancing SHACS with Oblivious RAM for Secure and Resilient Access Control in Cloud Healthcare Environments

Abstract

This study presents an enhanced security framework for cloud-based healthcare systems by 
integrating Oblivious RAM (ORAM) into Secure and Resilient Healthcare Access Control 
Systems (SHACS). The incorporation of ORAM provides access pattern obfuscation, preventing 
inference attacks and ensuring that sensitive patient data remains protected during access 
operations. The proposed system leverages adaptive access control, anomaly detection, and 
dynamic policy adaptation, which together improve the overall security and efficiency of 
healthcare data management. By using ORAM, the system ensures that data access patterns 
cannot be easily deduced, further protecting against unauthorized access and maintaining 
confidentiality. The performance evaluation of the proposed framework demonstrates several 
key improvements. The access latency is reduced to 43.5 milliseconds, and the system achieves a 
throughput of 124.3 requests per second, showcasing its ability to handle high-demand cloud 
healthcare environments. Additionally, the anomaly detection rate is significantly high at 96.2%, 
ensuring proactive identification of suspicious access patterns. The resilience score of the system 
stands at 94.7%, reflecting its robustness against both insider and external threats. The false
positive rate is minimized to 3.2%, improving the accuracy of anomaly detection. Further 
analysis through an ablation study reveals that combining ORAM with resilient mechanisms 
reduces privacy leakage to just 7.5 bits and enhances SHACS resilience to 98.1%. These results 
highlight the framework’s effectiveness in balancing security and performance, offering a robust, 
scalable solution that secures healthcare data in the cloud while mitigating modern cyber threats. 
This study underscores the potential of integrating advanced cryptographic techniques into 
healthcare systems for improved security and privacy.