Abstract

In the realm of complex industrial processes, optimizing efficiency through distributed algorithms is pivotal, yet vulnerable to adversarial attacks that aim to compromise the integrity and effectiveness of optimization processes. Adversarial attacks encompass various malicious strategies, including data poisoning, model evasion, and privacy breaches, which pose significant threats to the reliability and security of distributed optimization systems. To mitigate these risks, differential privacy emerges as a crucial safeguarding mechanism. By incorporating differential privacy into distributed optimization algorithms, sensitive data can be protected without compromising the accuracy of optimization outcomes. This abstract explores the role of differential privacy in countering adversarial threats, discusses implementation strategies such as noise injection and secure aggregation, and highlights real-world applications in smart manufacturing and energy grid management. Despite challenges in performance and integration complexity, the adoption of differential privacy promises to fortify industrial systems against adversarial attacks, ensuring robust and secure optimization processes in the face of evolving threats.