Differential privacy in machine learning is a method to ensure the privacy of individual data within a dataset. It operates under the principle that the outcome of data analysis or machine learning algorithms should not significantly change whether an individual's data is included or excluded. This method adds a layer of protection by introducing randomness into data analysis, making it nearly impossible to infer specific details about individuals from aggregated data. It's particularly valuable in fields like healthcare and finance, where sensitive data is handled, ensuring that while useful insights can be gleaned from large datasets, the confidentiality of individual data points is maintained.