Privacy-Preserving Technologies for Secure Data Management in Digital Systems: A Review of Encryption, Anonymization, Differential Privacy, Federated Learning and Secure Computation

Abstract

Digital platforms, cloud services, online transactions and data-driven applications collect and process large volumes of personal and confidential information every day, increasing the risk of data breaches, unauthorized access, identity theft and misuse. Protecting privacy while still allowing data to be used for legitimate purposes has therefore become a core requirement of modern systems. This paper reviews the principal privacy-preserving technologies and how they can be combined for secure data management. It covers data encryption at rest and in transit, anonymization models such as k-anonymity and l-diversity together with the risk of re-identification, differential privacy, which adds calibrated noise to protect individuals, federated learning, which trains models without centralizing raw data, homomorphic encryption, which allows computation on encrypted data, secure multi-party computation and trusted execution environments. Attacks such as membership inference that motivate these defences are discussed. A layered architecture for privacy-aware data management is proposed, with a research gap analysis, objectives and a comparison of techniques by protection level, performance cost and maturity. Regulatory drivers, including India's Digital Personal Data Protection Act 2023 and the GDPR, are considered. The review concludes that no single technique is sufficient and that layered combinations matched to the threat model provide the best balance of privacy, utility and performance.