What question did this study set out to answer?

The research aims to develop a method for optimizing privacy protection during cross-border data flows using federated learning.

March 14, 2026International Journal of Information and Communication Technology0 citationsOpen Access

Optimising privacy protection for cross-border data flow based on federated learning

FNFeifei NiuUniversity of Ottawa

Key Points

The research aims to develop a method for optimizing privacy protection during cross-border data flows using federated learning.
Developed a cross-border dataset for training
Designed five main modules for the privacy-preserving system
Utilized a neural network as the base model for federated training
Conducted experimental evaluations to measure system performance
Achieved an accuracy of 0.892
Demonstrated robustness with a score of 0.912
Outperformed standard methods in privacy protection
Effectively processed various types of data

Abstract

As cross-border data flows continue to expand, protecting people's privacy has become a major barrier to international cooperation.This research provides a privacy-preserving optimisation technique based on federated learning to address it.In this framework, a cross-border dataset is produced first, and then five main modules are designed.Then, a neural network is used as the base model for federated training to train a global model without sending any data outside of the country.Last, experimental evaluations check how well the system works.The results show that the proposed method has an accuracy of 0.892 and a robustness of 0.912, meaning it works well even while processing many types of data and protecting privacy.This is far better than standard methods.This technology offers a safe and effective way to work together on data across borders.

Ask AI

Helpful

Bookmark

View Full Paper