TRIDENT -- A Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks using Transformers, Adversarial Learning, and Differential Privacy

TL;DR

TRIDENT is a novel three-tier model that combines transformers, adversarial learning, and differential privacy techniques to detect propaganda on mobile platforms while safeguarding user privacy.

Contribution

It introduces a unified defense mechanism integrating syntactic obfuscation, label perturbation, and semantic variance for privacy-preserving propaganda detection.

Findings

Achieved F1 scores of 0.89 and 0.90 with baseline models

Reduced F1 to 0.83 with privacy-preserving techniques

Demonstrated effective privacy protection with minimal accuracy loss

Abstract

The proliferation of propaganda on mobile platforms raises critical concerns around detection accuracy and user privacy. To address this, we propose TRIDENT - a three-tier propaganda detection model implementing transformers, adversarial learning, and differential privacy which integrates syntactic obfuscation and label perturbation to mitigate privacy leakage while maintaining propaganda detection accuracy. TRIDENT leverages multilingual back-translation to introduce semantic variance, character-level noise, and entity obfuscation for differential privacy enforcement, and combines these techniques into a unified defense mechanism. Using a binary propaganda classification dataset, baseline transformer models (BERT, GPT-2) we achieved F1 scores of 0.89 and 0.90. Applying TRIDENT's third-tier defense yields a reduced but effective cumulative F1 of 0.83, demonstrating strong privacy protection across mobile ML deployments with minimal degradation.