A HYBRID FRAMEWORK FOR SECURE AND PRECISE DATA ACQUISITION IN MACHINE LEARNING SYSTEMS

Abstract

The efficacy of machine learning (ML) systems fundamentally depends on the quality, diversity, and security of training data. This paper presents a comprehensive framework addressing two critical objectives: employing advanced data acquisition methodologies and ML techniques for raw data collection and precision enhancement, and developing security protocols for data acquisition using ML. The proposed framework integrates multi-source data acquisition techniques including web scraping, API integration, sensor-based collection, and synthetic data generation with a multi-layered security architecture comprising data anonymization, privacy preserving training, and secure sharing protocols. We evaluate the framework through case studies in healthcare, finance, and cybersecurity domains, demonstrating significant improvements in data precision (22-30% enhancement) while maintaining robust security guarantees. The framework's modular design enables adaptability across diverse applications, contributing to the development of trustworthy and high-performance ML systems.