# Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

arXiv: https://arxiv.org/abs/2101.10569 · HTML: https://mlsanity.com/paper/2101.10569/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2021.

## Built on by (most cited first, 10 of 10)

- [FedMood: Federated Learning on Mobile Health Data for Mood Detection](https://mlsanity.com/paper/2102.09342/) (2021)
- [PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications](https://mlsanity.com/paper/2204.12064/) (2022)
- [Noise is All You Need: Private Second-Order Convergence of Noisy SGD](https://mlsanity.com/paper/2410.06878/) (2024)
- [Federated Learning with Differential Privacy: An Utility-Enhanced Approach](https://mlsanity.com/paper/2503.21154/) (2025)
- [ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation](https://mlsanity.com/paper/2508.18318/) (2025)
- [Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach](https://mlsanity.com/paper/2307.12157/) (2023)
- [Using Decentralized Aggregation for Federated Learning with Differential Privacy](https://mlsanity.com/paper/2311.16008/) (2023)
- [ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push](https://mlsanity.com/paper/2510.20157/) (2025)
- [Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing](https://mlsanity.com/paper/2512.09463/) (2025)
- [Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap](https://mlsanity.com/paper/2603.13570/) (2026)

## Abstract (arXiv)

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy.