SVM Learning for Default Prediction of Credit Card under Differential Privacy

Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice Pub Date : 2020-11-09 DOI:10.1145/3411501.3419431

Jianping Cai, Ximeng Liu, Yingjie Wu

引用次数: 4

Abstract

Currently, financial institutions utilize personal sensitive information extensively in machine learning. It results in significant privacy risks to customers. As an essential standard of privacy, differential privacy is often applied to machine learning in recent years. To establish a prediction model of credit card default under the premise of protecting personal privacy, we consider the problems of customer data contribution difference and data sample distribution imbalance, propose weighted SVM algorithm under differential privacy. Through theoretical analysis, we have ensured the security of differential privacy. The algorithm solves the problem of prediction result deviation caused by sample distribution imbalance and effectively reduces the data sensitivity and noise error. The experimental results show that the algorithm proposed in this paper can accurately predict whether a customer is default while protecting personal privacy.

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差分隐私下信用卡默认预测的SVM学习

目前，金融机构在机器学习中广泛使用个人敏感信息。这给客户带来了重大的隐私风险。差分隐私作为一种重要的隐私标准，近年来经常被应用到机器学习中。为建立保护个人隐私前提下的信用卡违约预测模型，考虑客户数据贡献差异和数据样本分布不平衡问题，提出差分隐私下的加权SVM算法。通过理论分析，我们保证了差分隐私的安全性。该算法解决了样本分布不平衡导致的预测结果偏差问题，有效降低了数据敏感性和噪声误差。实验结果表明，本文提出的算法能够在保护个人隐私的同时准确预测客户是否违约。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice

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