2021
DOI: 10.3233/ais-210602
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A trust evaluation model for secure data aggregation in smart grids infrastructures for smart cities

Abstract: New technologies and automation systems have changed the traditional smart grid systems into new and integrated intelligent systems. These new smart systems are adopted for energy efficiency, demand and response, management and control, fault recovery, reliability and quality of services. With various benefits, smart grids have vulnerabilities due to open communication systems, and open infrastructures. Smart grids systems are based on real-time services, where privacy and security id one of the major challeng… Show more

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Cited by 10 publications
(7 citation statements)
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“…Smart grid systems are based on real-time services, where privacy and security are one of the main challenges. In order to meet these challenges and deal with security and privacy issues, Qureshi et al proposed the Smart Grid Trust Evaluation Model (TEMSG) for the security data aggregation in smart grids and smart cities [10]. Rasool et al proposed an optimized combination of ferroelectric material parameters in the gate stack to achieve transmission characteristics without hysteresis and lower subthreshold swing.…”
Section: Introductionmentioning
confidence: 99%
“…Smart grid systems are based on real-time services, where privacy and security are one of the main challenges. In order to meet these challenges and deal with security and privacy issues, Qureshi et al proposed the Smart Grid Trust Evaluation Model (TEMSG) for the security data aggregation in smart grids and smart cities [10]. Rasool et al proposed an optimized combination of ferroelectric material parameters in the gate stack to achieve transmission characteristics without hysteresis and lower subthreshold swing.…”
Section: Introductionmentioning
confidence: 99%
“…In 2021 Qureshi et al [16] proposed the Trust Evaluation Model for Smart Grids (TEMSG) to secure the data collection in smart cities and smart grids. In smart grids, machine learning algorithms can be used to estimate faulty information and, consequently, to obtain trust values.…”
Section: Literature Reviewmentioning
confidence: 99%
“…However, the security analysis did not consider internal attacks conducted by dishonest agents, but rather, it included privacy, fairness, verifiability, and transparency of the system, demonstrating on-off trust and resistance to bad-mouthing. K. Qureshi et al [20] designed a trust evaluation model for smart grids (TEMSG) towards ensuring a secure aggregation of data from smart grids (SG) and smart cities. The authors adopted machine learning methods to collect trust data, estimate information, and then evaluate and verify the reliability and accuracy of the system.…”
Section: Related Workmentioning
confidence: 99%