Privacy-Preserving Machine Learning (PPML)

An overview of techniques and approaches for training and deploying machine learning models while preserving data privacy.

Overview

Privacy-Preserving Machine Learning (PPML) encompasses techniques that enable machine learning on sensitive data without compromising individual privacy.

Key Techniques

Federated Learning

Train models across decentralized data sources without centralizing the data.

Horizontal FL - Same features, different samples
Vertical FL - Same samples, different features
Federated Transfer Learning - Different features and samples

Differential Privacy

Add calibrated noise during training to provide formal privacy guarantees.

Secure Multi-Party Computation (SMPC)

Multiple parties jointly compute a function over their inputs while keeping inputs private.

Homomorphic Encryption

Perform computations on encrypted data without decrypting it.

Applications

Healthcare ML
Financial fraud detection
Cross-organization collaboration
Personal data analytics

Challenges

Computational overhead
Communication costs
Privacy-utility tradeoff
Model accuracy

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