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Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?
Auteur(s)
Date de parution
2019
In
Computing Research Repository (CoRR)
Vol.
1905.12298
Résumé
Based on differential privacy (DP) framework, we introduce and unify privacy definitions for the multi-armed bandit algorithms. We represent the framework with a unified graphical model and use it to connect privacy definitions. We derive and contrast lower bounds on the regret of bandit algorithms satisfying these definitions. We leverage a unified proving technique to achieve all the lower bounds. We show that for all of them, the learner's regret is increased by a multiplicative factor dependent on the privacy level ϵ. We observe that the dependency is weaker when we do not require local differential privacy for the rewards.
Identifiants
Type de publication
journal article
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