Dark Pool Information Leakage Detection through Natural Language Processing of Trader Communications

Abstract

This article presents a new approach to detecting data leaks in the trade environments of the dark swimming pool through advanced natural language processing for merchants. The study presents a comprehensive framework that integrates privacy's protective calculation techniques with sophisticated NLP models to identify potential information leak models while maintaining the confidentiality of merchants. The proposed system utilizes a multi-layered architecture incorporating transformer-based networks optimized for financial communication analysis, achieving a 96.8% detection rate with a false positive rate of 0.08%. The implementation employs differential privacy mechanisms with ε = 0.1 to protect trader identities while preserving aggregate pattern detection capabilities. Experimental validation using datasets comprising over 10 million trader communications from five major dark pool operators demonstrates significant improvements over existing methods. The system processes an average of 2.3 milliseconds per message in real time in real time, maintaining 97.51% of the system's availability. The study extends the value theory of Stratonovich's knowledge to quantify information leaks in economic communications, by setting up a new mathematical framework for market monitoring. The findings contribute to the development of more effective market surveillance strategies and support evidence-based regulatory policies. The system's practical implementation addresses critical challenges in balancing detection capabilities with privacy requirements in modern financial markets.