Privacy Assurance Routine Using Natural Attributes (PARUNA): A Hybrid Framework for Privacy Assurance

Abstract

Privacy is widely recognized as a fundamental human right, forming the cornerstone of individual autonomy and trust in digital interactions. However, safeguarding this right in today’s AI-driven, hyper-connected ecosystem presents significant challenges. The complexity arises from the sheer scale of data flows, algorithmic decision-making, and cross-border processing, which amplify risks of misuse and erosion of user control. Traditional privacy engineering techniquessuch as anonymization (removing identifiers from datasets), encryption (mathematical methods to secure data in transit and at rest), differential privacy (adding statistical noise to protect individual records), and synthetic data generation (creating artificial datasets for analysis)—offer essential technical defenses. Yet, these measures alone do not fully address broader dimensions like governance frameworks, regulatory compliance obligations, and human behavioral factors such as consent fatigue or trust perception.