.png){kind=icon}
.png){kind=icon}
Return
A user-friendly attribute-based data access control scheme for smart grids
.png){kind=icon}
OA
AI
DOI：10.1016/j.aej.2022.12.041.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Smart grids are envisioned as the next-generation electricity grids and can contribute to the overall economic and environmental health of our society. It is evident that effective use of the data measured from the grid is what makes the grid smart. Because the smart grid is a major issue of people's livelihood, the data measured from it is very sensitive. It is thus highly necessary to adopt data access control in smart grids to guarantee the security and privacy of the measured data. Due to its flexibility and scalability, attribute-based encryption (ABE) is widely utilized to realize data access control in smart grids. However, most existing ABE solutions impose a heavy decryp-tion overhead on their users, which hinders the widespread use of data and often causes the data to be left to sleep in the repository. To this end, we propose a user-friendly attribute-based data access control scheme for smart grids by adopting the idea of computation outsourcing. Under our proposed scheme, users can outsource a large amount of computation to a server during the decryption phase while still guaranteeing the security and privacy of the data. Theoretical analysis and experimental results demonstrate that our scheme outperforms the existing schemes by achiev-ing a very low decryption cost. We hope that our proposed scheme will facilitate the use of data measured in smart grids.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Keywords:
Data access control
Smart grid
Attribute -based encryption
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.8
Papers:
6.3K
Citations:
2.6W
Organization
No organization information available