Advanced Encryption Techniques in Biometric Payment Systems: A Big Data and AI Perspective

Abstract

In the rapidly evolving landscape of biometric payment systems, the integration of advanced encryption techniques is crucial for ensuring robust security and privacy. This paper explores cutting-edge encryption methodologies tailored for biometric data in the context of big data and artificial intelligence (AI) applications. We investigate how these advanced techniques address the unique challenges posed by the vast amounts of sensitive biometric information generated and processed in modern payment systems. The study provides an overview of various encryption strategies, including homomorphic encryption, secure multi-party computation, and quantum-resistant algorithms, evaluating their effectiveness in safeguarding biometric data against emerging threats. Additionally, the paper examines the role of AI in enhancing encryption mechanisms and optimizing performance, highlighting how machine learning models can predict and mitigate potential vulnerabilities. By analyzing real-world case studies and empirical data, we offer insights into the practical implementation of these technologies and their impact on the security landscape of biometric payments. This research contributes to a deeper understanding of how advanced encryption and AI can collaboratively fortify biometric payment systems, ultimately paving the way for more secure and privacy-preserving financial transactions.