研究垂直联邦学习中的安全算法，以安全逻辑回归为例。
A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example
- 在加密域中实现垂直联邦学习的模型训练
- 保护各方数据隐私，防止信息泄露
- 适合关注数据安全与隐私计算的研究者
进入大数据时代后，越来越多公司采用机器学习技术构建服务。然而，企业自行收集数据并提取有效特征成本高昂。虽然与其他公司数据结合可提升模型性能，但可能违反法律法规。因此，在数据共享与隐私保护之间取得平衡至关重要。本文聚焦分布式数据场景，针对垂直联邦学习框架开展安全模型训练研究。此处的‘安全’指整个训练过程在加密域中执行，从而缓解隐私担忧。
原文摘要 · Abstract (English)
After entering the era of big data, more and more companies build services with machine learning techniques. However, it is costly for companies to collect data and extract helpful handcraft features on their own. Although it is a way to combine with other companies' data for boosting the model's performance, this approach may be prohibited by laws. In other words, finding the balance between sharing data with others and keeping data from privacy leakage is a crucial topic worthy of close attention. This paper focuses on distributed data and conducts secure model training tasks on a vertical federated learning scheme. Here, secure implies that the whole process is executed in the encrypted domain. Therefore, the privacy concern is released.
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