Differential Privacy Techniques for Cyber Physical Systems: A Survey

Hassan, Muneeb Ul; Rehmani, Mubashir Husain; Chen, Jinjun

doi:10.48550/arxiv.1812.02282

Cited by 2 publications

(5 citation statements)

References 316 publications

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“…Privacy-preserving measures are generally not considered in industrial collaboration schemes [8]. However, applications of DP [5] and privacy in general [9] to industrial data sets are discussed in literature without edge computing schemes.…”

Section: Related Workmentioning

confidence: 99%

“…Originally conceived to mitigate the tension between the gain of knowledge and the protection of personally identifiable information (PII), DP can also be applied to use cases other than those involving PII [5]. DP allows privacypreserving data analytics by adding noise to data sets to provide a specific level of privacy while maintaining a certain level of meaningfulness to be able to detect patterns and derive statistical conclusions [6].…”

Section: Introductionmentioning

confidence: 99%

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Leveraging Edge Computing and Differential Privacy to Securely Enable Industrial Cloud Collaboration Along the Value Chain

Giehl

Heinl

Busch

2021

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

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Big data continues to grow in the manufacturing domain due to increasing interconnectivity on the shop floor in the course of the fourth industrial revolution. The optimization of machines based on either real-time or historical machine data provides benefits to both machine producers and operators. In order to be able to make use of these opportunities, it is necessary to access the machine data, which can include sensitive information such as intellectual property.Employing the use case of machine tools, this paper presents a solution enabling industrial data sharing and cloud collaboration while protecting sensitive information. It employs the edge computing paradigm to apply differential privacy to machine data in order to protect sensitive information and simultaneously allow machine producers to perform the necessary calculations and analyses using this data.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leveraging Edge Computing and Differential Privacy to Securely Enable Industrial Cloud Collaboration Along the Value Chain

Giehl

Heinl

Busch

2021

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

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“…Therefore, we define an edge resource in an Industrie 4.0 landscape as any resource located between the application area of CPS and cloud data centers whilst still within the local network and, consequently, in the control of the plant operator. For enhancing the control over the data before releasing them to the cloud data centers, sophisticated privacy models are available for industrial settings [6]. Those privacy models allow to protect IP and privacy of its employees [7].…”

Section: Cloud Servicesmentioning

confidence: 99%

“…Time-series data is another form of data frequently occurring in industrial applications. Approaches for general anonymization of time-series are given by [13], whereas approaches specific to manufacturing are discussed in [6]. However, requirements from SMEs or other companies are not considered.…”

Section: Cloud Data Centermentioning

confidence: 99%

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Edge-computing enhanced privacy protection for industrial ecosystems in the context of SMEs

Giehl

Schneider

Busch

et al. 2019

2019 12th CMI Conference on Cybersecurity and Privacy (CMI)

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The ongoing transformation of the manufacturing landscape introduces new business opportunities for enterprises but also brings new challenges with it. Especially small-and medium-sized companies (SMEs) require an increasing effort to stay competitive. Data produced on the shop-floor can be harnessed to conduct analyses useful to plant operators, e.g., for optimization of production capabilities or for increasing plant security. Therefore, we propose a privacy-preserving edgecomputing architecture to facilitate a platform for utilizing such applications. Our approach is motivated by requirements from SMEs in Germany, e.g., protection of intellectual property, and employs suitable privacy models. We demonstrate the viability of the proposed framework by evaluation of uses cases for machine chatter optimization and anomaly detection within plants.

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Differential Privacy Techniques for Cyber Physical Systems: A Survey

Cited by 2 publications

References 316 publications

Leveraging Edge Computing and Differential Privacy to Securely Enable Industrial Cloud Collaboration Along the Value Chain

Leveraging Edge Computing and Differential Privacy to Securely Enable Industrial Cloud Collaboration Along the Value Chain

Edge-computing enhanced privacy protection for industrial ecosystems in the context of SMEs

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