Horizontal multi-party data publishing via discriminator regularization and adaptive noise under differential privacy

Abstract

With the rapid proliferation of data collection and storage technologies, the growing demand for horizontal multi-party data publishing has created an urgent need for robust privacy-preserving mechanisms that can effectively handle sensitive distributed data across multiple organizations. While existing approaches attempt to address this challenge, they often fail to balance privacy protection with data utility, struggle to achieve effective information fusion across heterogeneous data distributions, and incur significant computational overhead. In this paper, we introduce the NATION approach, an innovative GAN-based framework that advances multi-party data publishing through sophisticated information fusion techniques while maintaining stringent differential privacy guarantees and computational efficiency. In NATION, we modify the traditional GAN architecture through a distributed design where multiple discriminators are strategically allocated across parties while centralizing the generator at a semi-trusted server, enabling seamless fusion of distributed knowledge with minimal computational cost. Building on this foundation, we introduce two key technical innovations: an iterative-aware adaptive noise IAN method that dynamically optimizes noise injection based on training convergence, and a global-aware discriminator regularization GDR method that leverages Bregman Divergence to enhance inter-discriminator information exchange while ensuring model stability. Through comprehensive theoretical analysis and extensive experimental evaluation on real-world datasets, we demonstrate that NATION consistently outperforms state-of-the-art approaches by up to 7% in accuracy while providing provable privacy guarantees, which makes a significant advancement in secure GAN-based information fusion for privacy-sensitive applications.