Accessibility of covariance information creates vulnerability in Federated Learning frameworks.

ABSTRACT:

Motivation

Federated Learning (FL) is gaining traction in various fields as it enables integrative data analysis without sharing sensitive data, such as in healthcare. However, the risk of data leakage caused by malicious attacks must be considered. In this study, we introduce a novel attack algorithm that relies on being able to compute sample means, sample covariances, and construct known linearly independent vectors on the data owner side.

Results

We show that these basic functionalities, which are available in several established FL frameworks, are sufficient to reconstruct privacy-protected data. Additionally, the attack algorithm is robust to defense strategies that involve adding random noise. We demonstrate the limitations of existing frameworks and propose potential

SUBMITTER: Huth M 

PROVIDER: S-EPMC10516515 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature