An Efficient and Privacy-Preserving Data Aggregation Scheme for Smart Grids in Cloud Environment

被引:0
作者
Rani P. [1 ]
Singh A.K. [1 ]
机构
[1] Department of Computer Applications, National Institute of Technology, Haryana, Kurukshetra
关键词
Data aggregation; Elliptic curve ElGamal cryptosystem; Privacy-preservation; Smart grid;
D O I
10.1007/s42979-023-01955-2
中图分类号
学科分类号
摘要
Smart grids are emerging as a crucial national infrastructure, as they support not only the power transmission but also the transmission of operational information. The smart meters are a fundamental element of the smart grid which record and transmit the power usage information of customers to the utility through intermediate aggregator nodes. Since the information communication is based on advanced digital technology, the smart grids are highly vulnerable to cyber-attacks. They require a highly secure and privacy preserving solutions for communication along with minimal computation and communication overheads. Additionally, the aggregator nodes require to verify the authenticity of the received consumption data, which is also supposed to be cost efficient. However, a lot of research solutions are being provided over the security and privacy issues through fully homomorphic and pairing based operations. These solutions lead to a heavy computation and communication costs. Thereby, to cater the crucial cybersecurity concerns of the grid system, an efficient, secure and privacy preserving scheme (EPDA) is proposed that is based on elliptic curve ElGamal cryptosystem. The performance analysis depicts the efficacy of the proposed scheme in terms of computation and communication costs and the storage overhead as well. The computation cost at the end of smart meter, fog node and control center is reduced by 57.88%, 98.07% and 93.23%, respectively, over state of the art works. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
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共 26 条
[1]  
Singh A.K., Kumar J., A secure and privacy-preserving data aggregation and classification model for smart grid, MultimedTools Appl, 82, pp. 22997-23015, (2023)
[2]  
Kumar J., Gupta R., Saxena D., Et al., Power consumption forecast model using ensemble learning for smart grid, JSupercomput, 79, pp. 11007-11028, (2023)
[3]  
Singh N., Gupta I., Singh A.K., Senso scale: A framework to preserve privacy over cloud using sensitivity range, In: Advances in Cyber Security and Intelligent Analytics, pp. 79-104, (2022)
[4]  
Gupta I., Gupta R., Singh A.K., Buyya R., Mlpam: A machine learning and probabilistic analysis based model for preserving security and privacy in cloud environment, IEEE Syst J, 15, 3, pp. 4248-4259, (2020)
[5]  
Singh A.K., Chhabra S., Gupta R., Saxena D., A reliable client detection system during load balancing for multi-tenant cloud environment, SN Comput Sci, 4, 1, (2022)
[6]  
Saxena D., Singh A., Security embedded dynamic resource allocation model for cloud data centre, Electron Lett, 56, 20, pp. 1062-1065, (2020)
[7]  
Gupta R., Gupta I., Singh A.K., Saxena D., Lee C.-N., An iot-centric data protection method for preserving security and privacy in cloud, IEEE Syst J, 17, 2, pp. 2445-2454, (2023)
[8]  
Gupta I., Singh A.K., An integrated approach for data leaker detection in cloud environment, J Inform Sci Eng, 36, 5, (2020)
[9]  
Saxena D., Singh A.K., Osc-mc: Online secure communication model for cloud environment, IEEE Commun Lett, 25, 9, pp. 2844-2848, (2021)
[10]  
Gupta I., Singh A.K., Lee C.-N., Buyya R., Secure data storage and sharing techniques for data protection in cloud environments: a systematic review, analysis, and future directions, IEEE Access, 10, pp. 71247-71277, (2022)