• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 58 Issue 1
Jan.  2023
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Article Contents
HONG Zhichao, LI Hui, ZHANG Jin, CHEN Haojie. Risk Evaluation Model of Blockchain Integration Based on CKKS Encryption Scheme[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 100-108. doi: 10.3969/j.issn.0258-2724.20210082
Citation: HONG Zhichao, LI Hui, ZHANG Jin, CHEN Haojie. Risk Evaluation Model of Blockchain Integration Based on CKKS Encryption Scheme[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 100-108. doi: 10.3969/j.issn.0258-2724.20210082

Risk Evaluation Model of Blockchain Integration Based on CKKS Encryption Scheme

doi: 10.3969/j.issn.0258-2724.20210082
  • Received Date: 28 Jan 2021
  • Rev Recd Date: 18 May 2021
  • Available Online: 30 Sep 2022
  • Publish Date: 26 May 2021
  • Aiming at solving problems of distrust in the data interaction between multiple parties and leakage of privacy data, an integrated risk assessment model based on the Cheon-Kim-Kim-Song (CKKS) fully homomorphic encryption scheme is proposed by using homomorphic encryption algorithm in combination with multiple assessment models for risk assessment. Firstly, the triangular fuzzy comprehensive evaluation method is used to determine weights of evaluation indexs, and a variety of evaluation methods are used to process the sample data in the distributed database to obtain the risk evaluation results of related nodes for the same transaction event. Secondly, the public key is used to encrypt the evaluation results, and then homomorphic operation is performed to obtain the encrypted comprehensive evaluation results, so as to avoid data leakage in the process of risk assessment. Thirdly, the private key is used to decode the evaluation results to obtain decrypted comprehensive evaluation results. Finally, 5000 samples from China Railway Express companies are taken as cases to evaluate the risk with eight common evaluation models, including the decision tree model, Adaboost model, Bagging model, ExtraTree model, gradient boosting regression tree (GBDT) model, K-nearest neighbor (KNN) model, random forest model, and support vector machine (SVM) model. In addition, the comprehensive evaluation results encrypted by CKKS scheme are compared with the results directly calculated by plaintext and encrypted by BFV scheme. The findings show that: the integrated risk assessment model is universal and applicable to more common assessment models; the error rates of the comprehensive evaluation results obtained by the integrated risk assessment model are small, within 10−9 against the actual results; compared with the result encrypted by BFV scheme, the error rate of the result encrypted by CKKS scheme is less than 1/100000 of the former, and the evaluation result is more accurate.

     

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