More than privacy: Adopting differential privacy in game-theoretic mechanism design
The vast majority of artificial intelligence solutions are founded on game theory, and
differential privacy is emerging as perhaps the most rigorous and widely adopted privacy …
differential privacy is emerging as perhaps the most rigorous and widely adopted privacy …
Advancements in federated learning: Models, methods, and privacy
Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …
concerns. Its main ingredient is to cooperatively learn the model among the distributed …
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 …
Privacy-preserving dynamic personalized pricing with demand learning
The prevalence of e-commerce has made customers' detailed personal information readily
accessible to retailers, and this information has been widely used in pricing decisions. When …
accessible to retailers, and this information has been widely used in pricing decisions. When …
A privacy-preserving distributed contextual federated online learning framework with big data support in social recommender systems
Nowadays, the booming demand of big data analytics and the constraints of computational
ability and network bandwidth have made it difficult for a stand-alone agent/service provider …
ability and network bandwidth have made it difficult for a stand-alone agent/service provider …
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 …
Private reinforcement learning with pac and regret guarantees
Motivated by high-stakes decision-making domains like personalized medicine where user
information is inherently sensitive, we design privacy preserving exploration policies for …
information is inherently sensitive, we design privacy preserving exploration policies for …
Locally differentially private (contextual) bandits learning
We study locally differentially private (LDP) bandits learning in this paper. First, we propose
simple black-box reduction frameworks that can solve a large family of context-free bandits …
simple black-box reduction frameworks that can solve a large family of context-free bandits …
When privacy meets partial information: A refined analysis of differentially private bandits
We study the problem of multi-armed bandits with ε-global Differential Privacy (DP). First, we
prove the minimax and problem-dependent regret lower bounds for stochastic and linear …
prove the minimax and problem-dependent regret lower bounds for stochastic and linear …
Differential privacy in personalized pricing with nonparametric demand models
In recent decades, the advance of information technology and abundant personal data
facilitate the application of algorithmic personalized pricing. However, this leads to the …
facilitate the application of algorithmic personalized pricing. However, this leads to the …