Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning (PPML) is 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 cryptography, statistics, and machine learning to allow data analysis without exposing raw information, addressing concerns in domains like healthcare, finance, and personal data. Common approaches include federated learning, differential privacy, homomorphic encryption, and secure multi-party computation.

Also known as: PPML, Privacy-Preserving ML, Private Machine Learning, Secure Machine Learning, Confidential ML

Why learn Privacy-Preserving Machine Learning?

Developers should learn PPML when building applications that handle sensitive data, such as in healthcare for patient records, finance for transaction analysis, or any scenario requiring compliance with regulations like GDPR or HIPAA. It enables collaboration on data without sharing it directly, reducing privacy risks and legal liabilities while still leveraging machine learning insights.

Learning Resources

• course: OpenMined Privacy-Preserving ML Course
• docs: Google's Federated Learning Paper
• course: Coursera: Privacy in the Digital Age
• tutorial: Differential Privacy Tutorial by Apple
• book: Book: The Algorithmic Foundations of Differential Privacy