Privacy Against Inference Attacks in Vertical Federated Learning
Vertical federated learning is considered, where an active party, having access to true class labels, wishes to build a classification model by utilizing more features from a passive party, which has no access to the labels, to improve the model accuracy. In the prediction phase, with logistic regression as the classification model, several inference attack techniques are proposed that the adversary, i.e., the active party, can employ to reconstruct the passive party's features, regarded as sensitive information. These attacks, which are mainly based on a classical notion of the center of a set, i.e., the Chebyshev center, are shown to be superior to those proposed in the literature. Moreover, several theoretical performance guarantees are provided for the aforementioned attacks. Subsequently, we consider the minimum amount of information that the adversary needs to fully reconstruct the passive party's features. In particular, it is shown that when the passive party holds one feature, and the adversary is only aware of the signs of the parameters involved, it can perfectly reconstruct that feature when the number of predictions is large enough. Next, as a defense mechanism, a privacy-preserving scheme is proposed that worsen the adversary's reconstruction attacks, while preserving the full benefits that VFL brings to the active party. Finally, experimental results demonstrate the effectiveness of the proposed attacks and the privacy-preserving scheme.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningInference AttackPrivacy PreservingVertical Federated LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks
Vertical Federated Learning (VFL) enables collaborative model training across organizations that share common user samples but hold disjoint feature spaces. Despite its potential, VFL is susceptible to feature inference …
Federated LearningLabel Inference Attacks Against Vertical Federated Learning
As the initial variant of federated learning (FL), horizontal federated learning (HFL) applies to the situations where datasets share the same feature space but differ in the sample space, e.g., the collaboration between…
Federated LearningVertical Federated LearningKDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
Vertical Federated Learning (VFL) is a category of Federated Learning in which models are trained collaboratively among parties with vertically partitioned data. Typically, in a VFL scenario, the labels of the samples ar…
Federated LearningKnowledge DistillationVertical Federated LearningA 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 LearningDefending against Reconstruction Attack in Vertical Federated Learning
Recently researchers have studied input leakage problems in Federated Learning (FL) where a malicious party can reconstruct sensitive training inputs provided by users from shared gradient. It raises concerns about FL si…
Federated LearningPrivacy PreservingReconstruction AttackVertical Federated Learning