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Found 5 papers, 1 papers with code
Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness
Our analysis, conducted across multiple unlearning benchmarks, reveals that these algorithms inconsistently fulfill their unlearning commitments due to two main issues: 1) unlearning new data can significantly affect the unlearning utility of previously requested data, and 2) approximate algorithms fail to ensure equitable unlearning utility across different groups.
How Does Selection Leak Privacy: Revisiting Private Selection and Improved Results for Hyper-parameter Tuning
Recent works propose a generic private solution for the tuning process, yet a fundamental question still persists: is the current privacy bound for this solution tight?
Preserving Node-level Privacy in Graph Neural Networks
In this study, we propose a solution that specifically addresses the issue of node-level privacy.
Differentially Private Non-convex Learning for Multi-layer Neural Networks
This paper focuses on the problem of Differentially Private Stochastic Optimization for (multi-layer) fully connected neural networks with a single output node.
Practical Differentially Private and Byzantine-resilient Federated Learning
In contrast, we leverage the random noise to construct an aggregation that effectively rejects many existing Byzantine attacks.
