Privacy
7 techniques
Protecting personal data and maintaining confidentiality in AI systems.
7 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Influence Functions | Algorithmic | Model Agnostic | Any | Influence functions quantify how much each training example influenced a model's predictions by computing the change in... | ||
| Synthetic Data Generation | Algorithmic | Model Agnostic | Any | Synthetic data generation creates artificial datasets that aim to preserve the statistical properties, distributions,... | ||
| Federated Learning | Algorithmic | Model Agnostic | Any | Federated learning enables collaborative model training across multiple distributed parties (devices, organisations, or... | ||
| Differential Privacy | Algorithmic | Model Agnostic | Any | Differential privacy provides mathematically rigorous privacy protection by adding carefully calibrated random noise to... | ||
| Homomorphic Encryption | Algorithmic | Model Agnostic | Any | Homomorphic encryption allows computation on encrypted data without decrypting it first, producing encrypted results... | ||
| Datasheets for Datasets | Documentation | Model Agnostic | Any | Datasheets for datasets establish comprehensive documentation standards for datasets, systematically recording creation... | ||
| Fairness GAN | Algorithmic | GAN | Any | A data generation technique that employs Generative Adversarial Networks (GANs) to create fair synthetic datasets by... |
Rows per page
Page 1 of 1