关键词: 纵向联邦学习, 安全求交, 匿踪求交, 多方安全计算, 匿踪学习

Abstract: As a key technical paradigm to achieve “data availability and invisibility”, the core process of vertical federated learning is sample alignment based on private set intersection. Although the private set intersection protects the privacy of nonintersected information, it can’t meet the privacy protection requirements of user IDs in the intersected set. This paper proposes a fully anonymous vertical federated learning framework based on anonymous alignment to ensure that no private information of each holder set will be disclosed during the whole process. An implementation framework based on secure multiparty computation is proposed for fully anonymous joint modeling. The high performance and low error characteristics of the framework are verified through experiments, indicating it can be better applied in practice.

Key words: vertical federated learning, private set intersection, anonymous alignment, secure multiparty computation, anonymous federated learning