Volume 18, No. 6, 2021
Machine Learning Approaches To Protecting Privacy In Data Mining
Archana C H , Dr. Koppula Srinivas Rao
Abstract
In the era of big data, the utilization of vast datasets for data mining tasks has become commonplace across various domains. However, this proliferation of data usage raises significant privacy concerns, particularly regarding the potential for the disclosure of sensitive information. This paper explores the intersection of machine learning techniques and privacy preservation strategies in the context of data mining. We investigate several machine-learning approaches designed to mitigate privacy risks while maintaining the utility of mined data. Through a comprehensive review of existing literature, we discuss various methods such as differential privacy, homomorphic encryption, and federated learning, highlighting their strengths, limitations, and applications in safeguarding privacy during data mining operations. Furthermore, we identify current challenges and opportunities for future research in this rapidly evolving field.
Pages: 9717-9721
Keywords: Machine Learning, Data Mining, Challenges, Opportunities, Sensitive Information.