DisparateVulnerability
Disparate Vulnerability to Membership Inference Attacks
A membership inference attack (MIA) against a machine-learning model enables an attacker to determine whether a given data record was part of the model's training data or not. DisparateVulnerability helps in measuring how much a given model can be inverted.
AnonymityProtection
Key facts
Maturity
Support
C4DT
Lab
Key facts
Maturity
Support
C4DT
Lab
- Technical
- Research papers
- Source code: Lab GitHub
- Last commit: 2022-01-04
Security and Privacy Engineering Laboratory
Security and Privacy Engineering Laboratory
The Security and Privacy Engineering Laboratory develops tools and methodologies to help engineers building systems that respect societal values, such as security, privacy or non discrimination. Currently, they are working on
- Machine Learning impact on society
- Evaluating privacy in complex systems
- Engineering privacy-preserving systems
This page was last edited on 2022-08-11.
This page was last edited on 2022-08-11.