Differential Privacy Techniques for Cyber Physical Systems: A Survey (1812.02282v3)

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.

• The paper demonstrates that differential privacy provides a robust mechanism to secure sensitive CPS data by adding calibrated noise that balances privacy and utility.
• The paper details application-specific strategies in energy, transportation, healthcare, and IIoT, showcasing tailored solutions against distinct cyber threats.
• The paper highlights practical challenges and future research directions, emphasizing optimal noise calibration, real-time data handling, and integration with emerging technologies.

Overview of "Differential Privacy Techniques for Cyber Physical Systems: A Survey"

The surveyed paper presents an extensive review of differential privacy methods applied within cyber-physical systems (CPSs), a rapidly evolving technology area integrating embedded computing and information and communication technology. Differential privacy, positioned as a viable privacy-preserving strategy, promises to balance privacy and utility in data sharing within CPSs. The paper's scope traverses diverse applications of CPSs—energy systems, transportation, healthcare and medical arenas, and the industrial Internet of Things (IIoT).

Key Insights and Methods

1. Core Challenges in CPSs Privacy:
   • CPS environments are fundamentally data-rich, capturing vast and delicate information across various domains. Security involves not just typical data breaches but also sophisticated passive attacks that could potentially infer significant personal data through traffic analysis or data correlation.
   • In response, conventional privacy mechanisms like encryption and anonymization have shown limitations concerning computational complexity and re-identification risks in anonymization. Hence, differential privacy emerges, offering robust theoretical underpinnings with noise addition mechanisms ensuring privacy while retaining data utility.
2. Application-Specific Implementations:
   • Energy Systems (Smart Grids): Differential privacy effectively aids in protecting real-time reporting and demand response data, mitigating threats like non-intrusive load monitoring (NILM) attacks. The paper discusses approaches such as battery-supported differential privacy and data modification techniques (e.g., employing Laplacian and Gaussian noise mechanisms).
   • Transportation Systems: The complex data exchanges within intelligent transportation systems (ITS) demand privacy-scale models. Differential privacy ensures data obfuscation in railway networks and vehicular communication, safeguarding user patterns and confidential vehicular status data.
   • Healthcare Systems: The paper places a premium on healthcare data protection, citing differential privacy's ability to safeguard real-time patient monitoring systems and electronic health records. This section highlights improvements in data handling, error reduction, and secure processing frameworks.
   • IIoT Systems: In this domain, differential privacy addresses both the real-time operation data of industrial machinery and the stored data on cloud systems. It aims to preserve operational efficiency while protecting sensitive proprietary information.
3. Technological and Practical Implications:
   • The survey promotes differential privacy as a lightweight, computationally feasible solution for sectors increasingly integrated with IoT devices and cloud solutions. However, the future trajectory involves navigating complexities like optimal noise calculation, real-time data handling in resource-constrained environments, and maintaining operational responsiveness in industries.

Implications for Future Research and Development

The paper richly conveys the advancing landscape and unmet potential in applying differential privacy within CPSs. Future explorations should target refining this method to reduce utility loss, ensuring real-time applicability, and optimizing epsilon choices relevant to specific sectors, such as healthcare and critical infrastructures. The exploration of composition theorems in sequential data requests, integration with emerging models like blockchain, and enhancing the algorithmic efficiency will further enrich this domain.

In sum, the paper is a timely presentation of ongoing innovations and efforts aimed at securely realizing the benefits of CPSs through differential privacy, posing further research pathways for historical challenges in large-scale, heterogeneous data environments.