We design differentially private algorithms for the problem of online linear optimization in the full information and bandit settings with optimalÕ( √ T ) 1 regret bounds. In the full-information setting, our results demonstrate that (ε, δ)-differential privacy may be ensured for free -in particular, the regret bounds scale as O( √ T ) +Õ
In REF , the authors provide differentially private algorithms for the special case of online linear optimization with bandit feedback, and obtain regretÕ( √ T ǫ ) which is (almost) optimal.
The Price of Differential Privacy For Online Learning
ArXiv
ArXiv
Mathematics Computer Science