Privacy-Preserving Machine Learning refers to techniques that allow models to learn from data without compromising the privacy of individuals. This is crucial in fields like healthcare and finance, where sensitive information is often involved. Methods such as differential privacy and federated learning help ensure that personal data remains confidential while still enabling valuable insights. These approaches enable organizations to build effective machine learning models while adhering to privacy regulations and ethical standards. By minimizing the risk of data exposure, privacy-preserving techniques foster trust among users and encourage the sharing of data for research and development purposes.