Trustworthy distributed ai systems: Robustness, privacy, and governance
Emerging Distributed AI systems are revolutionizing big data computing and data
processing capabilities with growing economic and societal impact. However, recent studies …
processing capabilities with growing economic and societal impact. However, recent studies …
More than privacy: Applying differential privacy in key areas of artificial intelligence
Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However,
alongside all its advancements, problems have also emerged, such as privacy violations …
alongside all its advancements, problems have also emerged, such as privacy violations …
Differential privacy in the shuffle model: A survey of separations
A Cheu - arXiv preprint arXiv:2107.11839, 2021 - arxiv.org
Differential privacy is often studied in one of two models. In the central model, a single
analyzer has the responsibility of performing a privacy-preserving computation on data. But …
analyzer has the responsibility of performing a privacy-preserving computation on data. But …
Differentially-private federated linear bandits
A Dubey, AS Pentland - Advances in Neural Information …, 2020 - proceedings.neurips.cc
The rapid proliferation of decentralized learning systems mandates the need for differentially-
private cooperative learning. In this paper, we study this in context of the contextual linear …
private cooperative learning. In this paper, we study this in context of the contextual linear …
Federated bandit: A gossiping approach
In this paper, we study Federated Bandit, a decentralized Multi-Armed Bandit problem with a
set of N agents, who can only communicate their local data with neighbors described by a …
set of N agents, who can only communicate their local data with neighbors described by a …
Sok: differential privacies
D Desfontaines, B Pejó - arXiv preprint arXiv:1906.01337, 2019 - arxiv.org
Shortly after it was first introduced in 2006, differential privacy became the flagship data
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …
Differentially private contextual linear bandits
We study the contextual linear bandit problem, a version of the standard stochastic multi-
armed bandit (MAB) problem where a learner sequentially selects actions to maximize a …
armed bandit (MAB) problem where a learner sequentially selects actions to maximize a …
Federated linear contextual bandits with user-level differential privacy
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 …
differential privacy (DP). We first introduce a unified federated bandits framework that can …
Federated recommendation system via differential privacy
In this paper we are interested in what we term the federated private bandits framework, that
combines differential privacy with multi-agent bandit learning. We explore how differential …
combines differential privacy with multi-agent bandit learning. We explore how differential …
The price of differential privacy for online learning
We design differentially private algorithms for the problem of online linear optimization in the
full information and bandit settings with optimal $ O (T^{0.5}) $ regret bounds. In the full …
full information and bandit settings with optimal $ O (T^{0.5}) $ regret bounds. In the full …