Security-Aware Scheduling Methods for Distributed Systems with Integrated Motion Data Privacy Protection

INTRODUCTION: With the growth of Internet of Things (IoT) and edge computing, distributed systems are increasingly deployed across fields such as sports health and intelligent transportation. However, motion data, often containing sensitive personal information, poses significant privacy risks when handled in these systems. OBJECTIVES: This paper aims to propose a novel security-aware scheduling method that integrates motion data privacy protection in distributed systems. The goal is to balance system scheduling efficiency with robust privacy safeguards. METHODS: We introduce a framework that combines encryption technologies, privacy protocols, and dynamic scheduling algorithms. By embedding privacy protection constraints into the scheduling process, this method optimizes data transmission and storage during task execution. RESULTS: Experimental results demonstrate that the proposed approach effectively reduces privacy leakage risks by over 80% compared to classical greedy algorithms. While the mandatory cryptographic mechanisms introduce a marginal latency overhead, the system maintains highly competitive scheduling efficiency. When compared with state-of-the-art techniques such as pure DRL, the proposed system achieves a 71% reduction in privacy leakage probability, successfully balancing robust security with dynamic adaptability. CONCLUSION: This research presents an innovative solution for motion data privacy protection in distributed systems, offering significant improvements in both privacy and scheduling performance. The method's applicability extends to fields like IoT, smart health, and intelligent transportation, marking a crucial step toward more secure and efficient distributed systems.