一种安全高效的全匿踪纵向联邦学习方法OA北大核心CSTPCD

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.