A trust evaluation model for secure data aggregation in smart grids infrastructures for smart cities

2021 ◽  
pp. 1-18
Author(s):  
Kashif Naseer Qureshi ◽  
Muhammad Najam ul Islam ◽  
Gwanggil Jeon
Keyword(s):  
Data Aggregation ◽  
Smart Grids ◽  
Smart Cities ◽  
Evaluation Model ◽  
Security And Privacy ◽  
Trust Evaluation ◽  
Open Communication ◽  
Privacy And Security ◽  
Secure Data ◽  
Secure Data Aggregation

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 challenge. In order to address these challenges and deal with security and privacy issues, we proposed a Trust Evaluation Model for Smart Grids (TEMSG) for secure data aggregation in smart grids and smart cities. This model tackles privacy and security issues such as data theft, denial of services, data privacy and inside and outside attacks and malware attacks. Machine learning methods are used to gather trust values and then estimate the imprecise information to secure the data aggregation in smart grids. Experiments are conducted to evaluate and analyze the proposed model in terms of detection rate, trustworthiness, and accuracy.

scholarly journals Efficient and Privacy-Preserving Data Aggregation and Dynamic Billing in Smart Grid Metering Networks

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2085 ◽  
Author(s):  
An Braeken ◽  
Pardeep Kumar ◽  
Andrew Martin
Keyword(s):  
Smart Grid ◽  
Data Aggregation ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Smart Meters ◽  
Life Patterns ◽  
Consumption Data ◽  
Secure Data ◽  
Secure Data Aggregation ◽  
Privacy Issues

The smart grid enables convenient data collection between smart meters and operation centers via data concentrators. However, it presents security and privacy issues for the customer. For instance, a malicious data concentrator cannot only use consumption data for malicious purposes but also can reveal life patterns of the customers. Recently, several methods in different groups (e.g., secure data aggregation, etc.) have been proposed to collect the consumption usage in a privacy-preserving manner. Nevertheless, most of the schemes either introduce computational complexities in data aggregation or fail to support privacy-preserving billing against the internal adversaries (e.g., malicious data concentrators). In this paper, we propose an efficient and privacy-preserving data aggregation scheme that supports dynamic billing and provides security against internal adversaries in the smart grid. The proposed scheme actively includes the customer in the registration process, leading to end-to-end secure data aggregation, together with accurate and dynamic billing offering privacy protection. Compared with the related work, the scheme provides a balanced trade-off between security and efficacy (i.e., low communication and computation overhead while providing robust security).

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