Privacy Against Inference Attacks in Vertical Federated Learning

@article{Rassouli2022PrivacyAI,
  title={Privacy Against Inference Attacks in Vertical Federated Learning},
  author={Borzoo Rassouli and Morteza Varasteh and Deniz G{\"u}nd{\"u}z},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.11788}
}
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… 

Applications and Challenges of Federated Learning Paradigm in the Big Data Era with Special Emphasis on COVID-19

This work describes the novel applications and challenges of the FL paradigm with special emphasis on the COVID-19 pandemic, and describes the synergies of FL with other emerging technologies to accomplish multiple services to fight the COIDS pandemic.

References

SHOWING 1-10 OF 25 REFERENCES

Comprehensive Analysis of Privacy Leakage in Vertical Federated Learning During Prediction

A comprehensive analysis of privacy leakage in VFL frameworks during the prediction phase is conducted and a general gradient-based reconstruction attack framework is designed that can be flexibly applied to simple logistic regression models as well as multi-layer neural networks.

Feature Inference Attack on Model Predictions in Vertical Federated Learning

This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL and proposes two specific attacks on the logistic regression (LR) and decision tree (DT) models, according to individual prediction output.

A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression

This paper proposes a quasi-Newton method based vertical federated learning framework for logistic regression under the additively homomorphic encryption scheme and can considerably reduce the number of communication rounds with a little additional communication cost per round.

Communication-Efficient Learning of Deep Networks from Decentralized Data

This work presents a practical method for the federated learning of deep networks based on iterative model averaging, and conducts an extensive empirical evaluation, considering five different model architectures and four datasets.

Privacy-preserving Federated Brain Tumour Segmentation

The feasibility of applying differential-privacy techniques to protect the patient data in a federated learning setup for brain tumour segmentation on the BraTS dataset is investigated and there is a trade-off between model performance and privacy protection costs.

Protocols for secure computations

  • A. Yao
  • Mathematics
    23rd Annual Symposium on Foundations of Computer Science (sfcs 1982)
  • 1982
The author gives a precise formulation of this general problem and describes three ways of solving it by use of one-way functions, which have applications to secret voting, private querying of database, oblivious negotiation, playing mental poker, etc.

Data-Snooping Biases in Tests of Financial Asset Pricing Models

We investigate the extent to which tests of financial asset pricing models may be biased by using properties of the data to construct the test statistics. Specifically, we focus on tests using

Federated Learning for Mobile Keyboard Prediction

The federation algorithm, which enables training on a higher-quality dataset for this use case, is shown to achieve better prediction recall and the feasibility and benefit of training language models on client devices without exporting sensitive user data to servers are demonstrated.

Solving the Trust-Region Subproblem By a Generalized Eigenvalue Problem

It is demonstrated that the resulting algorithm is a general-purpose TRS solver, effective both for dense and large-sparse problems, including the so-called hard case, and obtaining approximate solutions efficiently when high accuracy is unnecessary.

Federated learning for privacy-preserving AI

This research presents an engineering and algorithmic framework to ensure data privacy and user confidentiality in the rapidly changing environment.