Secure and Efficient High- Frequency Trading In Cloud Computing: Leveraging LBPQC and PHE for Enhanced Data Protection and Performance

Abstract

Cloud computing has created a dramatic change in the banking sector. It is now possible to do cost-efficient, scalable, and high-performance online transactions using the applications of cloud computing. In addition to improving data storage, processing speed, and security, cloud computing reduces costs used in infrastructure. High latency, security risks, and compliance issues mar the traditional banking systems. These limitations made traditional banking inefficacious for recent financial applications such as High-Frequency Trading. This paper proposes a secure and efficient cloud-based high frequency trading (HFT) system based on Lattice-Based Post-quantum Cryptography (LBPQC) and Partially Homomorphic Encryption (PHE) for the provision of more secure transactions. The system minimizes both latency and computational expenses, thereby facilitating real-time executions while improving trade precision by 15% and reducing security breach chances by 20%. AI-driven compliance automation and anomaly detection are also complemented into the model for regulatory compliance. Much higher data protection as well as efficiency in the trade, and threat detection are possible with the proposal that will ensure effective and secure banking infrastructure for any cloud financial activity.