Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond

07/19/2022
by   Yuzheng Hu, et al.
5

We consider vertical logistic regression (VLR) trained with mini-batch gradient descent – a setting which has attracted growing interest among industries and proven to be useful in a wide range of applications including finance and medical research. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols might differ between one another, yet a procedure of obtaining local gradients is implicitly shared. We first consider the honest-but-curious threat model, in which the detailed implementation of protocol is neglected and only the shared procedure is assumed, which we abstract as an oracle. We find that even under this general setting, single-dimension feature and label can still be recovered from the other party under suitable constraints of batch size, thus demonstrating the potential vulnerability of all frameworks following the same philosophy. Then we look into a popular instantiation of the protocol based on Homomorphic Encryption (HE). We propose an active attack that significantly weaken the constraints on batch size in the previous analysis via generating and compressing auxiliary ciphertext. To address the privacy leakage within the HE-based protocol, we develop a simple-yet-effective countermeasure based on Differential Privacy (DP), and provide both utility and privacy guarantees for the updated algorithm. Finally, we empirically verify the effectiveness of our attack and defense on benchmark datasets. Altogether, our findings suggest that all vertical federated learning frameworks that solely depend on HE might contain severe privacy risks, and DP, which has already demonstrated its power in horizontal federated learning, can also play a crucial role in the vertical setting, especially when coupled with HE or secure multi-party computation (MPC) techniques.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/28/2023

VERTICES: Efficient Two-Party Vertical Federated Linear Model with TTP-aided Secret Sharing

Vertical Federated Learning (VFL) has emerged as one of the most predomi...
research
06/24/2021

Privacy Threats Analysis to Secure Federated Learning

Federated learning is emerging as a machine learning technique that trai...
research
05/02/2021

GRNN: Generative Regression Neural Network – A Data Leakage Attack for Federated Learning

Data privacy has become an increasingly important issue in machine learn...
research
07/24/2022

Privacy Against Inference Attacks in Vertical Federated Learning

Vertical federated learning is considered, where an active party, having...
research
11/22/2019

Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator

Federated Learning is a new distributed learning mechanism which allows ...
research
07/26/2023

Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings

The emergence of vertical federated learning (VFL) has stimulated concer...
research
12/08/2021

Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost

More and more orgainizations and institutions make efforts on using exte...

Please sign up or login with your details

Forgot password? Click here to reset