# Differential Privacy Techniques for Cyber Physical Systems: A Survey

arXiv: https://arxiv.org/abs/1812.02282 · HTML: https://mlsanity.com/paper/1812.02282/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## Built on by (most cited first, 21 of 21)

- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges](https://mlsanity.com/paper/2309.07438/) (2023)
- [LDP-Feat: Image Features with Local Differential Privacy](https://mlsanity.com/paper/2308.11223/) (2023)
- [Task-aware Privacy Preservation for Multi-dimensional Data](https://mlsanity.com/paper/2110.02329/) (2021)
- [PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications](https://mlsanity.com/paper/2204.12064/) (2022)
- [KL-regularization Itself is Differentially Private in Bandits and RLHF](https://mlsanity.com/paper/2505.18407/) (2025)
- [Dynamic Network-Assisted D2D-Aided Coded Distributed Learning](https://mlsanity.com/paper/2111.14789/) (2021)
- [Differentially Private Learning of Hawkes Processes](https://mlsanity.com/paper/2207.13741/) (2022)
- [DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2308.09902/) (2023)
- [Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Framework](https://mlsanity.com/paper/2410.03546/) (2024)
- [Noise is All You Need: Private Second-Order Convergence of Noisy SGD](https://mlsanity.com/paper/2410.06878/) (2024)
- [Synthesis of Dynamic Masks for Information-Theoretic Opacity in Stochastic Systems](https://mlsanity.com/paper/2502.10552/) (2025)
- [CloneShield: A Framework for Universal Perturbation Against Zero-Shot Voice Cloning](https://mlsanity.com/paper/2505.19119/) (2025)
- [Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse](https://mlsanity.com/paper/2202.08309/) (2022)
- [Differential Privacy: What is all the noise about?](https://mlsanity.com/paper/2205.09453/) (2022)
- [An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition](https://mlsanity.com/paper/2210.06382/) (2022)
- [TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments](https://mlsanity.com/paper/2211.00735/) (2022)
- [Differentially Private Distributed Estimation and Learning](https://mlsanity.com/paper/2306.15865/) (2023)
- [DP-SGD-Global-Adapt-V2-S: Triad Improvements of Privacy, Accuracy and Fairness via Step Decay Noise Multiplier and Step Decay Upper Clipping Threshold](https://mlsanity.com/paper/2312.02400/) (2023)
- [A Survey on Differential Privacy for SpatioTemporal Data in Transportation Research](https://mlsanity.com/paper/2407.15868/) (2024)
- [SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs](https://mlsanity.com/paper/2602.13529/) (2026)

## Abstract (arXiv)

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT).With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also needs certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.