  Due to the rising concerns on privacy protection, how to build machine
learning (ML) models over different data sources with security guarantees is
gaining more popularity. Vertical federated learning (VFL) describes such a
case where ML models are built upon the private data of different participated
parties that own disjoint features for the same set of instances, which fits
many real-world collaborative tasks. Nevertheless, we find that existing
solutions for VFL either support limited kinds of input features or suffer from
potential data leakage during the federated execution. To this end, this paper
aims to investigate both the functionality and security of ML modes in the VFL
scenario.
  To be specific, we introduce BlindFL, a novel framework for VFL training and
inference. First, to address the functionality of VFL models, we propose the
federated source layers to unite the data from different parties. Various kinds
of features can be supported efficiently by the federated source layers,
including dense, sparse, numerical, and categorical features. Second, we
carefully analyze the security during the federated execution and formalize the
privacy requirements. Based on the analysis, we devise secure and accurate
algorithm protocols, and further prove the security guarantees under the
ideal-real simulation paradigm. Extensive experiments show that BlindFL
supports diverse datasets and models efficiently whilst achieves robust privacy
guarantees.
