Description: Privacy-preserving machine learning refers to a set of techniques that allow machine learning models to be trained on data without compromising individuals’ privacy. These techniques are essential in a world where personal data protection is increasingly critical. Through methods such as federated learning, differential privacy, and homomorphic encryption, the aim is to ensure that sensitive data is not exposed during the training process. Federated learning, for instance, allows models to be trained on local devices, sending only the updated parameters to a central server instead of the data itself. Differential privacy, on the other hand, adds noise to the data to protect individuals’ identities, ensuring that the model’s outputs do not reveal specific information about them. These techniques are relevant for complying with privacy regulations, such as GDPR in Europe, and also foster user trust in applications that utilize artificial intelligence. In summary, privacy-preserving machine learning is a growing field that seeks to balance the need for data in model training with the imperative need to protect users’ personal information.
