Session Dependent Zero Knowledge Proof Technique for Enhanced Privacy Verification in Cloud-Based Electronic Health Records

Abstract

Electronic Healthcare Records (EHRs) provide distributed access to patient and doctor information through pervasive cloud-based storage. As this data is highly sensitive, robust privacy measures are essential to mitigate adversarial impacts. To ensure optimal privacy across multiple shared EHRs, this article proposes a Session-dependent Zero Knowledge Proof Technique (SZKPT). The framework identifies privacy breaches using two truth values: the first representing optimal session closure, and the second reflecting verification at each sharing instance. Both truth values are validated through iterated session validations, which are managed using a deep learning paradigm. During training, different combinations of truth values are employed to maximize privacy during data sharing, while iterative processes train consecutive validation instances to improve breach detection. Truth values are continuously updated to reflect the session closure and the most recent privacy verification. In practice, if either truth value equals zero, the session is suspended; otherwise, if truth values are valid in consecutive iterations, data sharing is delegated to the authorized user. The process is repeatted at regular intervals with updated truth values, ensuring continuous monitoring and adaptive privacy protection. The proposed technique is rigorously evaluated using key performance metrics, including access verification, computational complexity, privacy breach detection, verification time, and access delegation time. Results demonstrate that SZKPT effectively balances privacy preservation with usability, providing a reliable, scalable, and efficient solution for secure EHR management in cloud-based healthcare systems.