一种可信执行环境下的联邦逻辑回归评分卡系统

doi:10.3969/j.issn.0255-8297.2023.03.010

Abstract

Abstract: A federated logistic regression system under trusted execution environment is proposed to build a scorecard model while ensuring data privacy. This system uses the strong security of trusted execution environment to resist inference attacks in the process of parameter exchanging. Then, a joint normalization method and an improved federated average method are utilized to solve the problem of inconsistency of local data scale and improve the effectiveness of scorecard model under class imbalance condition, respectively. Test results on a public credit-overdue data set show that the improved federated average is more effective than typical federated average method in handling the problem of imbalanced class distribution. Compared with homomorphic encryption-based federated learning systems, the proposed federated logistic regression system performs a greatly improved execution efficiency.

Key words: logistic regression, federated learning, trusted execution environment, class imbalance

CLC Number:

TP391

SHI Wenze, LU Lin, QIN Wenjie, YU Tao. Federated Logistic Regression Scorecard System under Trusted Execution Environment[J]. Journal of Applied Sciences, 2023, 41(3): 488-499.

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Federated Logistic Regression Scorecard System under Trusted Execution Environment

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