PRIVACY-PRESERVING TECHNIQUES IN BIG DATA ANALYTICS

Abstract

Big data analytics has revolutionized decision-making by extracting valuable insights from massive datasets. However, it poses significant privacy risks due to the sensitive nature of data collected from individuals and organizations. This article reviews prominent privacy-preserving techniques in big data analytics, focusing on their principles, advantages, and limitations. Emphasis is placed on approaches such as anonymization, differential privacy, homomorphic encryption, and secure multi-party computation. The study contextualizes these techniques within Pakistan’s regulatory landscape and data ecosystem, providing practical recommendations for balancing analytics utility with privacy protection. The article also highlights recent advancements and future challenges in privacy-preserving big data analytics.