Protocols allowing parties to jointly compute functions while keeping inputs private.
This concept is essential for understanding specialized ai topics and forms a key part of modern AI systems.
Related Concepts
- Privacy
- Security
- Cryptography
Reference 3 stops to get here
Secure Multi-Party Computation
Protocols allowing parties to jointly compute functions while keeping inputs private.
Your route here
3 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training Data ✓ understood
The examples a model learns its weights from, kept separate from the validation and test data used to check how well it generalizes.
- Privacy-Preserving ML ✓ understood
Techniques for training and deploying models while protecting individual privacy (federated learning, differential privacy).
- Secure Multi-Party Computation · you are here ✓ understood
Where it sits
Before this
Privacy-Preserving ML Secure Multi-Party Computation
Leads to
Nothing yet: a destination in its own right.
Explore nearby
Shipping AI Homomorphic Encryption Encryption allowing computation on encrypted data, enabling private model inference. Shipping AI Trusted Execution Environment Secure hardware areas for protected computation, used for private AI inference. Shipping AI Federated Learning Training models across decentralized devices holding local data, without exchanging the data itself, preserving privacy. Shipping AI Differential Privacy A mathematical framework for quantifying and limiting privacy loss when releasing information about datasets.