Designing and Developing Utility Based Privacy Preserving System Using Data Mining Over Big Data in Cloud Systems and Environments.

Abstract

The rapid growth of big data and its integration into cloud systems has revolutionized data- driven decision-making. However, the inherent privacy risks associated with data storage, sharing, and processing have become a significant challenge. This study focuses on designing and developing a utility-based privacy-preserving system that leverages data mining techniques to safeguard sensitive information while maintaining data usability in cloud environments. The proposed system employs advanced anonymization, encryption, and differential privacy mechanisms to ensure data confidentiality without compromising analytical accuracy. By utilizing scalable data mining algorithms, the system is capable of handling the complexities and volume of big data in a distributed cloud infrastructure. Furthermore, the system incorporates a utility-driven model to balance privacy preservation with data utility, enabling organizations to extract meaningful insights while adhering to regulatory and ethical standards. Experimental evaluations on real-world datasets demonstrate the system's effectiveness in mitigating privacy risks while retaining high levels of data utility. This innovative approach provides a robust framework for privacy-preserving data mining in cloud systems, addressing the growing concerns of data security and privacy in the era of big data. The proposed solution has practical applications in domains such as healthcare, finance, and e-governance, where privacy and utility are critical.