Privacy-Preserving Data Sharing in Big Data Analytics: A Distributed Computing Approach

Abstract

Big data science has and is rapidly growing in the industries and is known to help in data analysis and forecasting. But the growth of data sharing in organizations creates many privacy problems especially in distributed environments. Discussing privacy preserving data sharing in Big Data analytics, this paper concentrates on distributed computing methods. It discusses techniques including encryption enhancement methods, differential privacy techniques, federated learning, and data access control techniques that maintain security and analysis capability. Also, it goes deeper into distributed structures such as blockchain and edge computing that allow secure sharing of the data. Healthcare, finance, and smart cities are used as examples of these techniques in real-world contexts throughout the paper, stressing on the application of approaches to reduce privacy threats. Four issues, namely scalability, the computational load required when processing large datasets, adversarial attacks, and the potential solutions or improvements that may combat them, are presented. The paper concludes with future directions, such as quantum computing and real time analytics providing the guide path for developing sound privacy preserving methodologies in the Big Data domain. This research underlines the concerns regarding data use and protection, and serves as a starting point for analyzing possibilities of secure, moral and creative data sharing.