Privacy Attacks on AI Systems
Privacy attacks exploit AI models to extract sensitive information about training data, model architecture, or individual data points.
Membership Inference
Determine if a specific data point was used in the model's training set
- • Confidence-based attacks
- • Shadow model training
- • Metric-based inference
Model Inversion
Reconstruct training data or sensitive features from model outputs
- • Gradient-based inversion
- • Query-based reconstruction
- • Feature extraction attacks
Data Extraction
Extract verbatim training data from language models and other AI systems
- • Prompt-based extraction
- • Memorization exploitation
- • Training data leakage
Attack Scenarios
Healthcare AI
Attackers could infer whether a patient's medical records were used to train a diagnostic model, revealing sensitive health information
Language Models
Large language models may memorize and leak training data including personal information, API keys, or proprietary content
Facial Recognition
Model inversion attacks can reconstruct facial images from face recognition models, compromising biometric privacy
Privacy-Preserving Techniques
Differential Privacy
- • DP-SGD training algorithm
- • Privacy budget management
- • Noise injection mechanisms
- • Privacy accounting
Secure Computation
- • Federated learning
- • Homomorphic encryption
- • Secure multi-party computation
- • Trusted execution environments