Data Privacy and Security Framework : Review and Synthesis - Jagdev, Aman Kumar; Dhiman, Anil Kumar (Indian Journal of Computer Science, 2025)
Challenges to the protection of personal and corporate information have multiplied with the rapid dissemination of digital technology and the growing reliance on information-based decision-making. Despite security and privacy having been extensively studied, existing systems at times treat them as separate concerns, resulting in fragmented solutions. This research analyzes existing practices of data privacy and security, reviews their strengths and weaknesses, and proposes a tiered model encompassing organizational, architectural, computational, and governance dimensions. The model focuses on accountability, transparency, and compliance through the application of security measures such as encryption, authentication, and audit mechanisms, as well as privacy-conserving techniques such as homomorphic encryption, federated learning, multi-party computation, and differential privacy. Future directions for research in explainable privacy, confidential computing, and policy automation are outlined, along with trade-offs between utility, efficiency, and user confidence.