Poster
Wed Aug 09 01:30 AM -- 05:00 AM (PDT) @ Gallery #68
The Price of Differential Privacy For Online Learning
In
Posters Wed
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Summary/Notes]
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-information setting, our results demonstrate that ε-differential privacy may be ensured for free -- in particular, the regret bounds scale as O(T^0.5+1/ε). For bandit linear optimization, and as a special case, for non-stochastic multi-armed bandits, the proposed algorithm achieves a regret of O(T^0.5/ε), while the previously best known regret bound was O(T^{2/3}/ε).