Data Privacy Concerns in Machine Learning: Solutions and Challenges

Abstract

Data privacy concerns in machine learning (ML) are significant, as the growing use of personal and sensitive data for model training introduces risks of privacy breaches. The integration of advanced ML techniques in various sectors—such as healthcare, finance, and education—raises concerns regarding how data is collected, processed, and stored. This paper discusses the primary data privacy challenges faced by ML systems, evaluates existing solutions to safeguard personal information, and explores future directions in privacy-preserving ML technologies. Key issues include data anonymization, secure data sharing, and the ethical use of AI. The paper also highlights the need for comprehensive regulatory frameworks and the role of differential privacy in protecting user data.