A Secure and Privacy-Preserving Framework for Healthcare Data Management Using Deterministic Additive Noise and Differential Privacy in Cloud Environments

The rapid adoption of cloud storage for healthcare data brings enhanced accessibility and streamlined data management, but it also introduces significant security and privacy challenges. This paper presents a multi-layered framework designed to securely manage and protect sensitive healthcare data in cloud environments. The proposed approach leverages encryption for securing data, combined with Deterministic Additive Noise (DAN) to enhance privacy during data storage and retrieval. Additionally, differential privacy mechanisms are employed to safeguard patient data during query processing, preventing unauthorized access while maintaining data utility. Key management is handled via a Hardware Security Module (HSM) to ensure the secure generation, storage, and usage of cryptographic keys. The framework also integrates secure authentication methods, including multi-factor authentication for both healthcare providers and patients, enabling secure data access and retrieval. This comprehensive approach strengthens the Confidentiality, Integrity, and Availability (CIA) of healthcare data in cloud environments, ensuring compliance with stringent privacy regulations such as HIPAA.