Citation :

Abinaya Pandiyarajan, Manonmani Thayanithi, Senthil Kumar Jegatheesaperumal, Abinaya Devi Chandrasekar, "Privacy-Preserving Healthcare Data Analytics using Homomorphic Operations and Context-Aware Access Management," International Journal of Electronics and Communication Engineering, vol. 13, no. 5, pp. 312-324, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I5P125

Abstract

One of the biggest challenges in healthcare analytics is striking the right balance between data privacy and using sensitive data. Traditional methods also tend to compromise information protection or analytical utility. To counter this, the proposed model applies homomorphic encryption in order to ensure that electronic health records are secure and that calculations are done on encrypted information without exposing unencrypted information relating to patients. This approach offers access control to encrypted data in the cloud, unlike the previous methods, based on the Open Policy Agent (OPA) in conjunction with the Role-Based Access Control (RBAC). This integration allows better security along with dynamic, context-aware policy enforcement. The design of homomorphic subtraction, derived from a homomorphic addition, contributes significantly to increasing the computational versatility. The findings reveal that the processing time of homomorphic multiplication of 180 KB of data reduces by 86.13 percent in comparison with the current procedures. Moreover, contrary to the static systems, OPA has a Policy-Centric Paradigm, which enables flexible and fine-grained permissions. The framework provides a safe, effective paradigm for healthcare research while protecting patient privacy and consent by combining cutting-edge encryption with adaptable policy enforcement.

Keywords

Homomorphic Encryption, Role-Based-Access-Control, Open Policy Agent, Sensitive Data, Healthcare.

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