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

arXiv: https://arxiv.org/abs/2008.01916 · HTML: https://mlsanity.com/paper/2008.01916/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 23 of 23)

- [One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy](https://mlsanity.com/paper/2203.06580/) (2022)
- [Machine Unlearning: A Survey](https://mlsanity.com/paper/2306.03558/) (2023)
- [Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks](https://mlsanity.com/paper/2102.05975/) (2021)
- [A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks](https://mlsanity.com/paper/2406.06186/) (2024)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning](https://mlsanity.com/paper/2309.10283/) (2023)
- [GeoLocator: a location-integrated large multimodal model for inferring geo-privacy](https://mlsanity.com/paper/2311.13018/) (2023)
- [Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey](https://mlsanity.com/paper/2309.16398/) (2023)
- [HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption](https://mlsanity.com/paper/2403.14111/) (2024)
- [Boosting Model Inversion Attacks with Adversarial Examples](https://mlsanity.com/paper/2306.13965/) (2023)
- [New Challenges in Reinforcement Learning: A Survey of Security and Privacy](https://mlsanity.com/paper/2301.00188/) (2022)
- [Vertical Federated Unlearning via Backdoor Certification](https://mlsanity.com/paper/2412.11476/) (2024)
- [Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation](https://mlsanity.com/paper/2406.12516/) (2024)
- [Test & Evaluation Best Practices for Machine Learning-Enabled Systems](https://mlsanity.com/paper/2310.06800/) (2023)
- [Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey](https://mlsanity.com/paper/2310.10665/) (2023)
- [Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives](https://mlsanity.com/paper/2406.10884/) (2024)
- [Security and Privacy Issues of Federated Learning](https://mlsanity.com/paper/2307.12181/) (2023)
- [Memorization of Named Entities in Fine-tuned BERT Models](https://mlsanity.com/paper/2212.03749/) (2022)
- [Towards Efficient Target-Level Machine Unlearning Based on Essential Graph](https://mlsanity.com/paper/2406.10954/) (2024)
- [Differentially Private GANs for Generating Synthetic Indoor Location Data](https://mlsanity.com/paper/2404.07366/) (2024)
- [AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions](https://mlsanity.com/paper/2509.11151/) (2025)
- [Generative Adversarial Networks Applied for Privacy Preservation in Biometric-Based Authentication and Identification](https://mlsanity.com/paper/2509.20024/) (2025)
- [Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions](https://mlsanity.com/paper/2607.24589/) (2026)

## Abstract (arXiv)

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.