Enhancing Collaborative Machine Learning for Security and Privacy in Federated Learning - Zhu, Mingwei; Yuan, Jiaqiang; Wang, Guanghui; Xu, Zheng; Wei, Kuo (Journal of Theory and Practice of Engineering Science, 2024)
In the age of the Internet, machine learning has revolutionized our lives, offering convenience and innovation. However, it also poses significant security and privacy risks that cannot be overlooked. With the vast amount of personal data we upload online, including search histories and location data, there's a growing concern about how this information is collected and potentially exploited by hackers or malicious actors.Moreover, in the realm of machine learning, there's a risk of attackers stealing training data and model results, which can disrupt algorithms and lead to substantial economic losses or even threats to human safety. Thus, safeguarding the security and privacy of machine learning processes has become paramount.This paper delves into the myriad security challenges and privacy risks associated with machine learning algorithms. It explores methods and technologies for securing federated learning, offering technical solutions to protect privacy while maintaining the efficiency and effectiveness of machine learning systems.