A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example
This work addresses privacy issues in vertical federated learning for companies needing to collaborate on data while complying with laws, though it appears incremental as it applies existing encryption methods to a specific scenario.
The paper tackles the challenge of training machine learning models on distributed data across companies without sharing raw data, using secure logistic regression as an example, and demonstrates that the process can be executed in an encrypted domain to address privacy concerns.
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.