Visible to the public A Successive Framework: Enabling Accurate Identification and Secure Storage for Data in Smart Grid

TitleA Successive Framework: Enabling Accurate Identification and Secure Storage for Data in Smart Grid
Publication TypeConference Paper
Year of Publication2020
AuthorsCao, S., Zou, J., Du, X., Zhang, X.
Conference NameICC 2020 - 2020 IEEE International Conference on Communications (ICC)
Keywordsblockchain, blockchain technology, CBFM, cloud computing, cloud-blockchain fusion model, Computer vision, cryptography, data deletion, Data models, data modification, data visualisation, Distributed databases, Fabric, image data, malicious eavesdropping, Meter reading, Parallel vision, parallel visual system, performance evaluations, power aware computing, power data, power engineering computing, privacy, pubcrawl, quality assurance, Scalability, secure manners, Secure storage, security guarantee, Smart grid, Smart grids, smart power grids, Substations, tamper-proof characteristics
AbstractDue to malicious eavesdropping, forgery as well as other risks, it is challenging to dispose and store collected power data from smart grid in secure manners. Blockchain technology has become a novel method to solve the above problems because of its de-centralization and tamper-proof characteristics. It is especially well known that data stored in blockchain cannot be changed, so it is vital to seek out perfect mechanisms to ensure that data are compliant with high quality (namely, accuracy of the power data) before being stored in blockchain. This will help avoid losses due to low-quality data modification or deletion as needed in smart grid. Thus, we apply the parallel vision theory on the identification of meter readings to realize accurate power data. A cloud-blockchain fusion model (CBFM) is proposed for the storage of accurate power data, allowing for secure conducting of flexible transactions. Only power data calculated by parallel visual system instead of image data collected originally via robot would be stored in blockchain. Hence, we define the quality assurance before data uploaded to blockchain and security guarantee after data stored in blockchain as a successive framework, which is a brand new solution to manage efficiency and security as a whole for power data and data alike in other scenes. Security analysis and performance evaluations are performed, which prove that CBFM is highly secure and efficient impressively.
Citation Keycao_successive_2020