  Artificial Intelligence (AI) has attracted a great deal of attention in
recent years. However, alongside all its advancements, problems have also
emerged, such as privacy violations, security issues and model fairness.
Differential privacy, as a promising mathematical model, has several attractive
properties that can help solve these problems, making it quite a valuable tool.
For this reason, differential privacy has been broadly applied in AI but to
date, no study has documented which differential privacy mechanisms can or have
been leveraged to overcome its issues or the properties that make this
possible. In this paper, we show that differential privacy can do more than
just privacy preservation. It can also be used to improve security, stabilize
learning, build fair models, and impose composition in selected areas of AI.
With a focus on regular machine learning, distributed machine learning, deep
learning, and multi-agent systems, the purpose of this article is to deliver a
new view on many possibilities for improving AI performance with differential
privacy techniques.
