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

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.

References in corpus (3)

• RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
• Improving fairness in machine learning systems: What do industry practitioners need?
• Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination

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• Adversarial Attacks Against Deep Generative Models on Data: A Survey
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
• Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey
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• Differentially Private GANs for Generating Synthetic Indoor Location Data
• Memorization of Named Entities in Fine-tuned BERT Models