  Federated learning (FL) enables distributed participants to collaboratively
learn a global model without revealing their private data to each other.
Recently, vertical FL, where the participants hold the same set of samples but
with different features, has received increased attention. This paper first
presents one label inference attack method to investigate the potential privacy
leakages of the vertical logistic regression model. Specifically, we discover
that the attacker can utilize the residue variables, which are calculated by
solving the system of linear equations constructed by local dataset and the
received decrypted gradients, to infer the privately owned labels. To deal with
this, we then propose three protection mechanisms, e.g., additive noise
mechanism, multiplicative noise mechanism, and hybrid mechanism which leverages
local differential privacy and homomorphic encryption techniques, to prevent
the attack and improve the robustness of the vertical logistic regression.
model. Experimental results show that both the additive noise mechanism and the
multiplicative noise mechanism can achieve efficient label protection with only
a slight drop in model testing accuracy, furthermore, the hybrid mechanism can
achieve label protection without any testing accuracy degradation, which
demonstrates the effectiveness and efficiency of our protection techniques
