基于CKKS加密方案的区块链集成风险评价模型

洪治潮; 李卉; 张锦; 陈豪杰

doi:10.3969/j.issn.0258-2724.20210082

基于CKKS加密方案的区块链集成风险评价模型

doi: 10.3969/j.issn.0258-2724.20210082

洪治潮^1,,
李卉¹,
张锦^{1, 2, 3, ,},
陈豪杰¹

1.
西南交通大学交通运输与物流学院，四川成都 610031
2.
西南交通大学综合交通大数据应用技术国家工程实验室，四川成都 610031
3.
西南交通大学综合交通运输智能化国家地方联合工程实验室，四川成都 610031

基金项目: 四川省科技厅重点研发项目（2018GZ0465）；中国铁路总公司技术研发项目（2014X011-B）；成都市科技局重点技术创新研发项目（2019-YF05-02059-GX）

详细信息

作者简介:
洪治潮（1994—），男，博士研究生，研究方向为智慧交通物流大数据，E-mail：hfs-xiaohong@my.swjtu.edu.cn

通讯作者:
张锦（1963—），男，教授，博士，研究方向为智慧交通物流大数据、物流系统规划、供应链金融，E-mail：zhjswjtu@swjtu.edu.cn

中图分类号: F275.1；TP399
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出版历程
- 收稿日期: 2021-01-28
- 修回日期: 2021-05-18
- 网络出版日期: 2022-09-30
- 刊出日期: 2021-05-26

Risk Evaluation Model of Blockchain Integration Based on CKKS Encryption Scheme

HONG Zhichao^1
,,
LI Hui¹,
ZHANG Jin^{1, 2, 3
, ,},
CHEN Haojie¹

1.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
2.
National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 610031, China
3.
National and Combined Engineering Lab of Intelligentizing Integrated Transportation, Southwest Jiaotong University, Chengdu 610031, China

摘要

摘要:
针对区块链平台中存在的多方数据交互不可信以及隐私数据易泄露等问题，基于CKKS （Cheon-Kim-Kim-Song）全同态加密方案，提出了一种集成风险评价模型，把同态加密算法应用到风险评价中，将多种评价模型与同态加密结合起来. 首先，利用三角模糊综合评价方法确定各评价指标的权重，通过多种评价方法处理分布式数据库中的样本数据，获得相关节点对同一交易事件的风险评价结果；其次，利用公钥对评价结果进行加密并进行同态运算，获得密文综合评价结果，以避免风险评价过程中的数据泄露；再次，利用私钥对评价结果进行解码，获得明文综合评价结果；最后，选取5 000个中欧班列企业的样本数据作为案例，利用决策树模型、Adaboost模型、Bagging模型、ExtraTree极端随机数模型、GBDT （gradient boosting regression trees）模型、KNN（K-nearest neighbor）模型、随机森林模型、SVM （support vector machine）模型等最为常见的评价模型进行风险评价，并将经CKKS方案加密后的综合评价结果与明文直接计算的综合评价结果和经BFV （Brakerski-Fan-vercauteren）方案加密后的综合评价结果进行了对比. 结果表明：该集成风险评价模型具有普适性，对较为常见的评价模型均能适用；模型的综合评价结果误差率较小，与实际结果的误差率均在10⁻⁹以内；与BFV方案加密后的结果相比，经CKKS方案加密后的结果误差率小于前者的十万分之一，评价结果更为准确.
- 区块链 /
- 数据泄露 /
- 全同态加密方案 /
- 风险评价 /
- 集成模型
Abstract:
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.
- Blockchain /
- data leakage /
- fully homomorphic encryption scheme /
- risk assessment /
- integrated model

HTML全文

图 1 问题描述与模型构建思路

Figure 1. Problem description and model construction

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图 2 风险评价流程

Figure 2. Risk assessment process

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图 3 回归结果

Figure 3. Regression results

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图 4 CKKS加密机制基于不同评价模型结果的误差率结果

Figure 4. Comparison of error rates of encrypted results by CKKS scheme based on different assessment models

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图 5 加密结果的误差率箱线图

Figure 5. Box plot of error rates of encryption results

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图 6 CKKS方案评价结果误差率分布

Figure 6. Error rate distribution of evaluation results by CKKS scheme

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图 7 BFV方案评价结果误差率分布

Figure 7. Error rate distribution of evaluation results by BFV scheme

下载: 全尺寸图片幻灯片

表 1 0.1～0.9标度的含义

Table 1. Meaning of scale 0.1～0.9

标度	对应的三角模糊数	含义
0.1	（0.1，0.1，0.2）	指标 i 相对于指标 j 极端不重要
0.3	（0.2，0.3，0.4）	指标 i 相对于指标 j 明显不重要
0.5	（0.4，0.5，0.6）	指标 i 与指标 j 同样重要
0.7	（0.6，0.7，0.8）	指标 i 相对于指标 j 明显重要
0.9	（0.8，0.9，0.9）	指标 i 相对于指标 j 极端重要

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表 2 定性指标量化评分表

Table 2. Quantitative scoring of qualitative indices

定性指标	评价指标分档/分
定性指标	[8, 10]	[4, 8)	[0, 4)
交易履约情况（X₈）	好	中	差
核心企业的对外担保状况（X₉）	几乎无	少量	较多
供应链关系的强度（X₁₀）	高	中	低

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表 3 三角模糊打分表

Table 3. Triangular fuzzy scoring results

k	B₁	B₂	B₃	B₄	B₅
1	（0.1，0.2，0.3）	（0，0.1，0.3）	（0.1，0.3，0.5）	（0.5，0.2，0.3）	（0.6，0.8，0.9）
2	（0.2，0.3，0.3）	（0.4，0.7，0.8）	（0.3，0.2，0.2）	（0.7，0.8，0.9）	（0.1，0.2，0.4）
3	（0.4，0.5，0.3）	（0.1，0.3，0.5）	（04，0.5，0.7）	（0.1，0.4，0.6）	（0.1，0.6，0.7）
4	（0.5，0.7，0.3）	（0.3，0.6，0.9）	（0.4，0.6，0.8）	（0.3，0.5，0.7）	（0.1，0.3，0.4）
5	（0.6，0.4，0.3）	（0.2，0.4，0.5）	（0.5，0.8，0.9）	（0.5，0.6，0.8）	（0.2，0.4，0.5）
w_m	（0.15， 0.21，0.32）	（0.13，0.22，0.35）	（0.11，0.18，0.28）	（0.18，0.25，0.39）	（0.09，0.14，0.24）

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表 4 指标赋权结果

Table 4. Index weighting results

指标名称	变量	权重	指标名称	变量	权重
企业规模	X₁	0.0246	核心企业对外担保情况	X₉	0.1512
销售利润率	X₂	0.0833	供应链关系强度	X₁₀	0.0014
速动比率	X₃	0.0255	赊销周期	X₁₁	0.1146
存货周转率	X₄	0.0117	产品可替代性	X₁₂	0.0884
资产负债率	X₅	0.0140	权益乘数	X₁₃	0.0176
交易量	X₆	0.0748	质押物变现能力	X₁₄	0.0799
交易金额	X₇	0.0363	总资产周转率	X₁₅	0.1329
交易履约情况	X₈	0.1437

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表 5 CKKS方案和BFV方案同态加密结果及误差率

Table 5. Homomorphic encryption results and error rates of CKKS and BFV schemes

编号	KNN 模型 + 随机森林模型	CKKS 全同态加密评价结果	CKKS 方案误差率	BFV 全同态加密评价结果	BFV 方案误差率
1	48.6117737805	48.6117737789	3.34 × 10⁻¹¹	48.6094744655	4.73 × 10⁻⁵
2	38.4173642857	38.4173642916	1.53 × 10⁻¹⁰	38.3934853887	6.22 × 10⁻⁴
3	66.6095043478	66.6095043506	4.09 × 10⁻¹¹	66.6003539367	1.37 × 10⁻⁴
4	40.7277690992	40.7277690999	1.66 × 10⁻¹¹	40.7098999864	4.39 × 10⁻⁴
5	36.0318162194	36.0318162184	2.58 × 10⁻¹¹	36.0170565909	4.10 × 10⁻⁴
6	39.2235467290	39.2235467305	3.98 × 10⁻¹¹	39.1413627605	2.10 × 10⁻³
7	45.2551751634	45.2551751619	3.20 × 10⁻¹¹	45.2384881180	3.69 × 10⁻⁴
8	85.5722701987	85.5722701993	7.54 × 10⁻¹²	85.5655826327	7.82 × 10⁻⁵
9	59.7378161329	59.7378161324	8.14 × 10⁻¹²	59.7254652272	2.07 × 10⁻⁴
10	49.4582034252	49.4582034281	5.74 × 10⁻¹¹	49.4489389677	1.87 × 10⁻⁴
11	50.6262683040	50.6262683016	4.62 × 10⁻¹¹	50.6237741777	4.93 × 10⁻⁵
12	59.0855247219	59.0855247221	4.30 × 10⁻¹²	59.0248410817	1.03 × 10⁻³
13	50.8218075188	50.8218075195	1.39 × 10⁻¹¹	50.8154754434	1.25 × 10⁻⁴
14	51.5208750000	51.5208750014	2.77 × 10⁻¹¹	51.5204698244	7.86 × 10⁻⁶
15	40.6476813842	40.6476813860	4.43 × 10⁻¹¹	40.6440481786	8.94 × 10⁻⁵
16	41.8885428177	41.8885428182	1.16 × 10⁻¹¹	41.8792671831	2.21 × 10⁻⁴
17	37.0070685441	37.0070685404	1.01 × 10⁻¹⁰	36.9933666065	3.70 × 10⁻⁴
18	66.6304171240	66.6304171283	6.33 × 10⁻¹¹	66.6209557226	1.42 × 10⁻⁴
19	14.8742302481	14.8742302472	6.03 × 10⁻¹¹	14.8490706101	1.69 × 10⁻³
20	38.9383642857	38.9383642857	3.34 × 10⁻¹¹	38.8372207385	2.60 × 10⁻⁵

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