2019/05/29 by Debabrota Basu, Basu, Debabrota, Christos Dimitrakakis +3 · 4 citations
Computer Science · Decision Sciences · #62L10 #94A15 #Advanced Bandit Algorithms Research #Age of Information Optimization #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.1905.12298
openalex publication_date 2019/05/29 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
Based on differential privacy (DP) framework, we introduce and unify privacy\ndefinitions for the multi-armed bandit algorithms. We represent the framework\nwith a unified graphical model and use it to connect privacy definitions. We\nderive and contrast lower bounds on the regret of bandit algorithms satisfying\nthese definitions. We leverage a unified proving technique to achieve all the\nlower bounds. We show that for all of them, the learner's regret is increased\nby a multiplicative factor dependent on the privacy level \ε. We\nobserve that the dependency is weaker when we do not require local differential\nprivacy for the rewards.\n