Machine Learning Privacy

Machine learning privacy involves protecting individuals' personal data used to train AI models. Since these models learn from large amounts of information, there's a risk that sensitive details could be unintentionally exposed or misused. Privacy techniques, such as data anonymization, encryption, and differential privacy, help ensure that personal information remains confidential. The goal is to enable AI systems to learn and improve while respecting individuals' rights and maintaining trust, balancing the benefits of AI with the need for data security and privacy.