Residue-based Label Protection Mechanisms in Vertical Logistic Regression
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
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningInference AttackregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
Federated learning (FL) has emerged as a practical solution to tackle data silo issues without compromising user privacy. One of its variants, vertical federated learning (VFL), has recently gained increasing attention a…
Federated LearningPrivacy PreservingVertical Federated LearningAsymmetrical Vertical Federated Learning
Federated learning is a distributed machine learning method that aims to preserve the privacy of sample features and labels. In a federated learning system, ID-based sample alignment approaches are usually applied with f…
Federated LearningVertical Federated LearningFeature Inference Attack on Model Predictions in Vertical Federated Learning
Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other. Recently, vertical FL, where the participating organizations…
Federated LearningInference AttackPredictionVertical Federated LearningGAN-based Vertical Federated Learning for Label Protection in Binary Classification
Split learning (splitNN) has emerged as a popular strategy for addressing the high computational costs and low modeling efficiency in Vertical Federated Learning (VFL). However, despite its popularity, vanilla splitNN la…
Binary ClassificationFederated LearningVertical Federated LearningTree-based Models for Vertical Federated Learning: A Survey
Tree-based models have achieved great success in a wide range of real-world applications due to their effectiveness, robustness, and interpretability, which inspired people to apply them in vertical federated learning (V…
Federated LearningSurveyVertical Federated Learning