Probability Density Functions
Ethics & Safety
Techniques and methods that enable machine learning while protecting sensitive data and maintaining individual privacy.
Detailed Explanation
Privacy-Preserving Machine Learning (PPML) encompasses techniques designed to protect sensitive data during model training and deployment. It ensures that individual information remains confidential, often using methods like federated learning, differential privacy, and secure multiparty computation. PPML allows organizations to build effective AI systems without compromising user privacy or data security, fostering trust and compliance with data protection regulations.
Use Cases
- •Enables secure collaborative healthcare predictions without exposing patient data across hospitals, ensuring compliance and safeguarding individual privacy.
Related Terms
Other terms in the Ethics & Safety category