Blockchain-based Secure and Reliable Manufacturing System

Bhattacharjee, Arpan; Badsha, Shahriar; Sengupta, Shamik

doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00052

Cited by 11 publications

(1 citation statement)

References 20 publications

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“…The goal of privacy-preserving techniques is to protect private information against being published or revealed by unauthorized as well as authorized users [14]. Several privacy-preserving approaches such as encryptionbased, perturbation-based, authentication-based and technologies like blockchain have been proposed in the literature to protect the sensitive information of CPS effectively [15]- [19]. Regarding privacy preservation works in CPS, Gope et al [20] proposed a privacy-preserving scheme to analyze the energy consumption data of end-users securely.…”

Section: Related Workmentioning

confidence: 99%

Vulnerability Characterization and Privacy Quantification for Cyber-Physical Systems

Bhattacharjee¹,

Badsha²,

Tamjid³

et al. 2021

Preprint

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Cyber-physical systems (CPS) data privacy protection during sharing, aggregating, and publishing is a challenging problem. Several privacy protection mechanisms have been developed in the literature to protect the sensitive data from adversarial analysis and eliminate the risk of re-identifying the original properties of shared data. However, most of the existing solutions have drawbacks, such as (i) lack of a proper vulnerability characterization model to accurately identify where privacy is needed, (ii) ignoring data providers privacy preference, (iii) using uniform privacy protection which may create inadequate privacy for some provider while overprotecting others, and (iv) lack of a comprehensive privacy quantification model assuring data privacy-preservation. To address these issues, we propose a personalized privacy preference framework by characterizing and quantifying the CPS vulnerabilities as well as ensuring privacy. First, we introduce a standard vulnerability profiling library (SVPL) by arranging the nodes of an energy-CPS from maximum to minimum vulnerable based on their privacy loss. Based on this model, we present our personalized privacy framework (PDP) in which Laplace noise is added based on the individual node's selected privacy preferences. Finally, combining these two proposed methods, we demonstrate that our privacy characterization and quantification model can attain better privacy preservation by eliminating the trade-off between privacy, utility, and risk of losing information.

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Section: Related Workmentioning

confidence: 99%

Vulnerability Characterization and Privacy Quantification for Cyber-Physical Systems

Bhattacharjee¹,

Badsha²,

Tamjid³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Toward Secure Cyber Infrastructure for Smart Cities: Learning-Based Intelligent Solutions

Islam

Bhattacharjee

Badsha

2022

IoT and WSN Based Smart Cities: A Machine Learning Perspective

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Blockchain Applications

Yang

Kelarev

et al. 2022

Blockchain Foundations and Applications

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is a distinguished expert in blockchain technologies and system modeling. He has a Ph.D. in Computer Sciences and has successfully initiated and participated in numerous projects, displaying exceptional skills as a concept designer, data architect, and agile manager. His expertise extends to blockchain, data processing, nonlinear systems modeling, and fintech. Currently holding positions at the Financial University under the Government of the Russian Federation, Dr. Chernyshenko continues to contribute to the evolution of blockchain technologies by supervising young researchers and collaborating with academia in quantitative finance, business analysis, and data modeling. He leads research projects, develops distributed database solutions and predictive models for oil cargo transportation analytics, and supervises a startup focused on AI-based forestry assessment.

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Blockchain-based Secure and Reliable Manufacturing System

Cited by 11 publications

References 20 publications

Vulnerability Characterization and Privacy Quantification for Cyber-Physical Systems

Vulnerability Characterization and Privacy Quantification for Cyber-Physical Systems

Toward Secure Cyber Infrastructure for Smart Cities: Learning-Based Intelligent Solutions

Blockchain Applications

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