PRIVACY ENHANCING TECHNOLOGIES: SAFEGUARDING DIGITAL RIGHTS IN THE DATA-DRIVEN ERA

Abstract

The exponential growth in data collection and processing, driven by ubiquitous computing, AI, and IoT, has intensified privacy concerns globally. Privacy Enhancing Technologies (PETs) offer a critical suite of technical solutions designed to minimize personal data exposure while enabling valuable data analysis and functionality. This paper comprehensively reviews the core principles, prominent technologies, current applications, challenges, and future research directions of PETs. We analyze foundational techniques including Homomorphic Encryption (HE), Secure Multi-Party Computation (MPC), Zero-Knowledge Proofs (ZKPs), Differential Privacy (DP), Federated Learning (FL), and Anonymous Communication networks. We evaluate their strengths, limitations, computational overhead, and suitability for different use cases (e.g., confidential computing, privacy-preserving AI, anonymous transactions). The paper highlights the increasing integration of PETs within regulatory frameworks like GDPR and CCPA and examines emerging challenges posed by advanced AI and quantum computing. We argue that PETs are evolving from niche tools to essential components of ethical data ecosystems, necessitating continued research in efficiency, usability, standardization, and quantum resistance to realize their full potential in protecting fundamental digital rights.