Cloud-based Federated Boosting for Mobile Crowdsensing

05/09/2020
by   Zhuzhu Wang, et al.
9

The application of federated extreme gradient boosting to mobile crowdsensing apps brings several benefits, in particular high performance on efficiency and classification. However, it also brings a new challenge for data and model privacy protection. Besides it being vulnerable to Generative Adversarial Network (GAN) based user data reconstruction attack, there is not the existing architecture that considers how to preserve model privacy. In this paper, we propose a secret sharing based federated learning architecture FedXGB to achieve the privacy-preserving extreme gradient boosting for mobile crowdsensing. Specifically, we first build a secure classification and regression tree (CART) of XGBoost using secret sharing. Then, we propose a secure prediction protocol to protect the model privacy of XGBoost in mobile crowdsensing. We conduct a comprehensive theoretical analysis and extensive experiments to evaluate the security, effectiveness, and efficiency of FedXGB. The results indicate that FedXGB is secure against the honest-but-curious adversaries and attains less than 1 XGBoost model.

READ FULL TEXT
research
07/24/2019

Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing

The state-of-the-art federated learning brings a new direction for the d...
research
04/15/2023

Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates

The privacy-sensitive nature of decentralized datasets and the robustnes...
research
07/14/2020

Privacy Preserving Text Recognition with Gradient-Boosting for Federated Learning

Typical machine learning approaches require centralized data for model t...
research
06/26/2023

Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing

Gaussian process regression (GPR) is a non-parametric model that has bee...
research
08/17/2021

Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation

With the increasing demands for privacy protection, privacy-preserving m...
research
02/07/2022

Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over Vertically Partitioned Dataset with Outsourced Computations

Due to privacy concerns, multi-party gradient tree boosting algorithms h...
research
02/22/2018

Privacy-Preserving Boosting with Random Linear Classifiers for Learning from User-Generated Data

User-generated data is crucial to predictive modeling in many applicatio...

Please sign up or login with your details

Forgot password? Click here to reset