Privacy Preserving Skellam Mixture Model Fusion for Robust Statistical Learning

Abstract

In an era of extensive data collection, preserving individual privacy while deriving actionable insights is a critical challenge. This study proposes a Privacy-Preserving Skellam Mixture Matrix Fusion (PPS MMF) framework enhanced by a Bidirectional Encoder Representations from Transformers (BERT) model. The PPS MMF leverages the Skellam distribution to obscure sensitive information during data fusion, ensuring privacy preservation. By integrating BERT, the framework captures nuanced contextual information, improving the accuracy of downstream tasks. Operating in a decentralized manner, this approach mitigates centralized data breach risks and enables secure data fusion across domains like healthcare, finance, and social media. Experimental results validate its effectiveness and practicality in real-world applications.