mlsanityData to 7 Oct 2026

Differential Privacy Techniques for Cyber Physical Systems: A Survey

arXiv 1812.02282 ↗First posted 6 Dec 2018cs.CRcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Dec 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 5; 2024: 4; 2025: 3; 2026: 1.

21 papers.

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  1. 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
  2. 2023Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challengescited by 14
  3. 2023LDP-Feat: Image Features with Local Differential Privacycited by 10
  4. 2021Task-aware Privacy Preservation for Multi-dimensional Datacited by 5
  5. 2022PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communicationscited by 3
  6. 2021Dynamic Network-Assisted D2D-Aided Coded Distributed Learningcited by 2
  7. 2025KL-regularization Itself is Differentially Private in Bandits and RLHFcited by 2
  8. 2022Differentially Private Learning of Hawkes Processescited by 1
  9. 2023DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learningcited by 1
  10. 2024Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Frameworkcited by 1
  11. 2024Noise is All You Need: Private Second-Order Convergence of Noisy SGDcited by 1
  12. 2025CloneShield: A Framework for Universal Perturbation Against Zero-Shot Voice Cloningcited by 1
9 more
  1. 2022Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inversecited by 0
  2. 2022Differential Privacy: What is all the noise about?cited by 0
  3. 2022An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognitioncited by 0
  4. 2022TorchFL: A Performant Library for Bootstrapping Federated Learning Experimentscited by 0
  5. 2023Differentially Private Distributed Estimation and Learningcited by 0
  6. 2023DP-SGD-Global-Adapt-V2-S: Triad Improvements of Privacy, Accuracy and Fairness via Step Decay Noise Multiplier and Step Decay Upper Clipping Thresholdcited by 0
  7. 2024A Survey on Differential Privacy for SpatioTemporal Data in Transportation Researchcited by 0
  8. 2025Synthesis of Dynamic Masks for Information-Theoretic Opacity in Stochastic Systemscited by 0
  9. 2026SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMscited by 0