Privacy-Preserving Techniques in Big Data Communication Networks: A Comprehensive Review

Abstract

Big data communication networks have revolutionized the way we handle vast amounts of information. However, they also pose significant privacy concerns, necessitating the development of robust privacy-preserving techniques. This review explores various privacy-preserving techniques in big data communication networks, examining their effectiveness, limitations, and potential areas for future research. A comprehensive evaluation of current methods, including cryptographic techniques, differential privacy, homomorphic encryption, and secure multiparty computation, is conducted. Furthermore, we provide a comparative analysis through tables, graphs, and diagrams for better understanding.