Search Results for author:
Found 8 papers, 2 papers with code
Digital Forgetting in Large Language Models: A Survey of Unlearning Methods
The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues are no longer present.
An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata
The alleged threat of reconstruction attacks has led the U. S. Census Bureau (USCB) to replace in the Decennial Census 2020 the traditional statistical disclosure limitation based on rank swapping with one based on differential privacy (DP).
GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)
This is a complex topic, and it is unreasonable to expect all TREs to be aware of all risks or that TRE researchers have addressed these risks in AI-specific training.
Enhanced Security and Privacy via Fragmented Federated Learning
To tackle the accuracy-privacy-security conflict, we propose {\em fragmented federated learning} (FFL), in which participants randomly exchange and mix fragments of their updates before sending them to the server.
Defending against the Label-flipping Attack in Federated Learning
The label-flipping (LF) attack is a targeted poisoning attack where the attackers poison their training data by flipping the labels of some examples from one class (i. e., the source class) to another (i. e., the target class).
A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning
We review the use of differential privacy (DP) for privacy protection in machine learning (ML).
Secure and Privacy-Preserving Federated Learning via Co-Utility
In this paper we build a federated learning framework that offers privacy to the participating peers as well as security against Byzantine and poisoning attacks.
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions
In contrast with centralized ML approaches, FL saves computation to the server and does not require the clients to outsource their private data to the server.
