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Multi-Armed Bandits with Local Differential Privacy
[article]
2020
arXiv
pre-print
This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee. In stochastic bandit systems, the rewards may refer to the users' activities, which may involve private information and the users may not want the agent to know. However, in many cases, the agent needs to know these activities to provide better services such as recommendations and news feeds. To handle this dilemma, we adopt differential privacy and
arXiv:2007.03121v1
fatcat:eehdco6p4ndpffatetx47o4nzi