Abstract
<jats:p>The exponential growth of big data and the rapid adoption of machine learning (ML) have introduced unprecedented opportunities for innovation but also significant privacy risks. Data aggregation, model inversion attacks, and inference threats increasingly compromise user confidentiality. This article reviews major privacy risks in big data mining and ML, explores technological and organizational solutions, and highlights future directions for ensuring data protection. Differential privacy, federated learning, encryption techniques, and policy frameworks are discussed as primary safeguards. A holistic approach combining technical, ethical, and legal strategies is emphasized to foster trust and security in data-driven systems. </jats:p>