We show that our method is highly effective in mitigating, or even eliminating, poisoning attacks on text classification, with only a small cost in predictive ...
Nov 7, 2021 · We show that our method is highly effective in mitigating, or even eliminating, poisoning attacks on text classification, with only a small cost ...
We show that our method is highly effective in mitigating, or even eliminating, poisoning attacks on text classification, with only a small cost in predictive ...
If the poisoned training data is available, the user can also choose re-train the model with differential private training (Abadi et al., 2016;Dwork et al., ...
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Nov 10, 2023 · Differential Privacy (DP) was originally developed to protect privacy. However, it has recently been utilized to secure machine learning. (ML) ...
Nov 8, 2021 · On-demand video platform giving you access to lectures from conferences worldwide.
Local Differential Privacy (LDP) protocols enable an un- trusted data collector to perform privacy-preserving data an- alytics. In particular, each user ...
By guaranteeing edge-differential privacy, DP-GCN allows users to analyze graph-structured data without leaking the sensitive connection information, such as.
A powerful category of (invisible) data poisoning attacks modify a subset of train- ing examples by small adversarial perturbations to change the prediction ...
Data poisoning is a targeted form of attack wherein an adversary deliberately manipulates the training data to compromise the efficacy of machine learning ...