[Paper Review] Differential Privacy Techniques for Cyber Physical Systems: A Survey

Muneeb Ul Hassan, Mubashir Husain Rehmani+1 more|arXiv (Cornell University)|Dec 6, 2018

Privacy-Preserving Technologies in DataComputer Science339 references21 citations

TL;DR

This survey presents a comprehensive analysis of differential privacy (DP) techniques for securing data in cyber-physical systems (CPSs), focusing on energy, transportation, healthcare, and industrial IoT applications. It demonstrates how DP’s noise-addition mechanism offers strong privacy guarantees with low computational overhead, while identifying key challenges in utility-privacy trade-offs and integration with emerging technologies like blockchain and edge computing.

ABSTRACT

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.

Motivation & Objective

• To address growing privacy threats in cyber-physical systems (CPSs) due to increasing data collection and complex system architectures.
• To evaluate the limitations of traditional privacy-preserving techniques like encryption and k-anonymity in CPS contexts.
• To establish differential privacy as a robust, mathematically grounded alternative for CPS data protection.
• To survey the implementation of DP across four key CPS domains: energy systems, transportation, healthcare, and industrial IoT.
• To identify open challenges and future research directions for enhancing DP in CPS environments.

Proposed method

• Systematic survey of differential privacy techniques applied in CPS applications, including real-time data, fog computing, and sensor networks.
• Application of noise injection mechanisms (e.g., Laplace, Gaussian) to protect sensitive data while preserving utility.
• Evaluation of DP’s privacy-utility trade-off through adjustable privacy parameters (ε) in various CPS workloads.
• Integration of DP with emerging technologies such as blockchain for secure, decentralized data sharing.
• Use of game theory to model and optimize the privacy-utility trade-off in multi-agent CPS environments.
• Analysis of computational efficiency and scalability of DP in resource-constrained CPS environments like edge and fog computing.

Experimental results

Research questions

• RQ1 How can differential privacy be effectively applied to protect sensitive data in cyber-physical systems across diverse domains?
• RQ2 What are the key challenges in balancing data utility and privacy preservation in CPS applications?
• RQ3 How do traditional privacy techniques like encryption and k-anonymity fall short in modern CPS environments?
• RQ4 What role can differential privacy play in securing data in smart grids, e-health systems, and industrial IoT deployments?
• RQ5 How can differential privacy be integrated with emerging technologies such as blockchain and edge computing to enhance CPS security?

Key findings

• Differential privacy provides strong, mathematically provable privacy guarantees with low computational overhead compared to encryption and anonymization.
• The use of noise injection via Laplace or Gaussian mechanisms enables fine-grained control over privacy-utility trade-offs through the privacy parameter ε.
• Traditional techniques like k-anonymity fail to ensure complete privacy due to re-identification risks, especially in high-dimensional datasets.
• Differential privacy is particularly effective in protecting real-time data in smart grids, e-health systems, and vehicular networks.
• Integration of differential privacy with blockchain can enhance transaction privacy in decentralized CPS environments.
• Future research should focus on combining DP with game theory and machine learning to optimize privacy-utility trade-offs in multi-agent CPS systems.