More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
It provides a new view on applying differential privacy to address broader AI issues such as security and fairness, but it is incremental as it synthesizes existing mechanisms without introducing new ones.
The paper tackles the problem of underutilizing differential privacy beyond privacy preservation in AI, showing it can improve security, stabilize learning, build fair models, and impose composition in areas like machine learning and deep learning.
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.