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

TL;DR

This paper explores how differential privacy extends beyond privacy preservation to enhance security, fairness, and stability in various AI domains, offering new perspectives on its broader applications.

Contribution

It provides a comprehensive overview of how differential privacy mechanisms can address multiple AI challenges beyond privacy, including security and fairness, across different AI systems.

Findings

Differential privacy can improve security in AI systems.

It helps in building fairer AI models.

Differential privacy stabilizes learning processes.

Abstract

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.