A SURVEY OF PRIVACY-PRESERVING ALGORITHMS IN BIG DATA SYSTEMS

The rapid expansion of Big Data systems has led to the collection of vast amounts of sensitive information across various domains such as healthcare, finance, and social media. As a result, preserving the privacy of this data has become a critical concern. This survey aims to explore the current privacy-preserving algorithms employed in Big Data systems. We provide an in-depth analysis of various techniques, such as differential privacy, homomorphic encryption, and secure multi-party computation, used to ensure data privacy while maintaining the utility of Big Data. The article also highlights the challenges associated with these techniques, including trade-offs between privacy and data utility. This paper serves as a comprehensive guide for researchers and practitioners interested in designing privacy-preserving solutions for Big Data applications.