Research Article
Blockchain-Based Outsourcing Shared Car Risk Prediction Scheme Design
@INPROCEEDINGS{10.1007/978-3-030-96791-8_1, author={Haonan Zhai and Song He and Zeyu Wei and Yong Xie}, title={Blockchain-Based Outsourcing Shared Car Risk Prediction Scheme Design}, proceedings={Security and Privacy in New Computing Environments. 4th EAI International Conference, SPNCE 2021, Virtual Event, December 10-11, 2021, Proceedings}, proceedings_a={SPNCE}, year={2022}, month={3}, keywords={Support vector machine learning Blockchain Homomorphic encryption Multi-key conversion protocol}, doi={10.1007/978-3-030-96791-8_1} }- Haonan Zhai
Song He
Zeyu Wei
Yong Xie
Year: 2022
Blockchain-Based Outsourcing Shared Car Risk Prediction Scheme Design
SPNCE
Springer
DOI: 10.1007/978-3-030-96791-8_1
Abstract
With the widespread use of shared cars, the security of cars and user data sharing have become increasingly important. To prevent the leakage of data and damage or non-return of shared cars, researchers have put forward many proposals of shared cars. But there are some problems in these schemes such as security, low precision. Therefore, we propose a shared car risk prediction scheme by using support vector machine (SVM) learning and blockchain, homomorphic encryption. First of all, this scheme adopts blockchain technology to ensure that the data can not be tampered with. ln addition, the homomorphic encryption algorithm is used to realize the machine learning calculation in the ciphertext state. Finally, the SVM learning algorithm is used to make the risk prediction results of shared cars more accurate. Through performance analysis and comparison, the scheme is proved to have higher accuracy and security.
