More Than Privacy Applying Differential Privacy in Key Areas of Artificial Intelligence


In recent years, there has been a lot of interest in the field of artificial intelligence, also known as AI. However, along with all of its advancements, new problems have also surfaced, such as violations of users' privacy, problems with model fairness, and security issues. Differential privacy, which is a promising mathematical model, has several appealing properties that can help solve these problems, which makes it quite a valuable tool. One of these properties is that it can help protect individuals' identities. Because of this, differential privacy has seen widespread use in the field of artificial intelligence (AI). However, to this day, no research has documented which differential privacy mechanisms can or have been leveraged to overcome the challenges posed by differential privacy, nor the properties that make this possibility possible. In this paper, we demonstrate that differential privacy is capable of more than simply preserving individuals' privacy. It is also possible to use it to improve security, stabilize learning, construct fair models, and impose composition in certain areas of artificial intelligence. With a particular emphasis on regular Machine Learning, distributed Machine Learning, Deep Learning, and multi-agent systems, the goal of this article is to present a fresh perspective on a wide variety of opportunities for enhancing the performance of artificial intelligence systems by utilizing differential privacy techniques.

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