Privacy-Preserving AI
Privacy-Preserving AI refers to a set of techniques and methodologies that enable machine learning models to be trained and deployed while protecting the privacy of sensitive data. It combines principles from artificial intelligence, cryptography, and data security to allow data analysis without exposing raw information. Common approaches include federated learning, differential privacy, homomorphic encryption, and secure multi-party computation.
Developers should learn Privacy-Preserving AI when building applications in healthcare, finance, or any domain handling sensitive personal data, as it helps comply with regulations like GDPR and HIPAA while enabling collaborative insights. It's crucial for scenarios where data cannot be centralized due to privacy concerns, such as training models across multiple hospitals or financial institutions without sharing patient or customer records.