🔒 Privacy-Preserving ML
Train models on sensitive data while protecting individual privacy
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0 / 5 completedIntroduction to Privacy-Preserving ML
🎯 The Privacy Challenge
Machine learning requires large datasets, but data often contains sensitive personal information. Privacy-preserving ML enables training models without exposing individual data points.
More privacy protection typically reduces model accuracy
🚨 Real-World Privacy Concerns
Medical Records
Patient data for disease prediction must remain confidential
Financial Data
Transaction histories for fraud detection contain PII
Mobile Devices
Keyboard predictions trained on personal messages
Search Queries
User searches reveal sensitive interests and behaviors
🛡️ Privacy Attack Scenarios
Model Inversion
Reconstruct training data from model parameters or predictions
Membership Inference
Determine if specific data was in the training set
Attribute Inference
Infer sensitive attributes not used during training
🔑 Key Privacy Techniques
Differential Privacy
Add calibrated noise to protect individual contributions
Federated Learning
Train on decentralized data without sharing raw data
Homomorphic Encryption
Compute on encrypted data without decryption
Secure MPC
Multiple parties compute without revealing inputs