  In the field of machine learning, many problems can be formulated as the
minimax problem, including reinforcement learning, generative adversarial
networks, to just name a few. So the minimax problem has attracted a huge
amount of attentions from researchers in recent decades. However, there is
relatively little work on studying the privacy of the general minimax paradigm.
In this paper, we focus on the privacy of the general minimax setting,
combining differential privacy together with minimax optimization paradigm.
Besides, via algorithmic stability theory, we theoretically analyze the high
probability generalization performance of the differentially private minimax
algorithm under the strongly-convex-strongly-concave condition. To the best of
our knowledge, this is the first time to analyze the generalization performance
of general minimax paradigm, taking differential privacy into account.
