Optimizing Data Pipelines for Privacy-Preserving Analytics in Cybersecurity

Abstract

In the evolving landscape of cybersecurity, the demand for advanced data analytics to detect, prevent, and respond to threats has significantly increased. However, this surge in data collection and analysis raises critical privacy concerns. This paper explores the optimization of data pipelines for privacy-preserving analytics within the realm of cybersecurity. By integrating advanced privacy-preserving techniques with data pipeline architectures, this research aims to enhance data security while maintaining the effectiveness of cybersecurity analytics. We propose a framework that combines data anonymization, secure multi-party computation, and differential privacy, offering a comprehensive approach to balancing privacy and analytical efficiency.