2 citations · 2 across the 1 of their papers we have counts for
4 papers · 1 filter
Federated Linear Contextual Bandits with User-level Differential Privacy
Ruiquan Huang, Huanyu Zhang, Luca Melis +3
This paper studies federated linear contextual bandits under the notion of user-level differential privacy (DP). We first introduce a unified federated bandits framework that can a…
Evaluating Privacy Leakage in Split Learning
Xinchi Qiu, Ilias Leontiadis, Luca Melis +2
Privacy-Preserving machine learning (PPML) can help us train and deploy models that utilize private information. In particular, on-device machine learning allows us to avoid sharin…
Differentially Private Query Release Through Adaptive Projection
Sergul Aydore, William Brown, Michael Kearns +4
We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like -way marginals, subject to differential privacy. Our…
Adversarial Robustness with Non-uniform Perturbations
Ecenaz Erdemir, Jeffrey Bickford, Luca Melis +1
Robustness of machine learning models is critical for security related applications, where real-world adversaries are uniquely focused on evading neural network based detectors. Pr…