Two-in-One Solution: Simultaneously Enhancing Security and Privacy for Data-Driven Models in Mobile Edge Computing
Pengrui Liu, Xiaohan Yuan, Wei Wang, Xiangrui Xu, Tao Li, Junyong Wang, Bin Wang, Witold Pedrycz · IEEE Transactions on Consumer Electronics · 2024
Data-driven models are widely employed in Mobile Edge Computing to satisfy the demands of Emerging Consumer Applications. However, previous work demonstrates that data-driven models are susceptible to security threats like Backdoor and Evasion Attacks or privacy threats like Membership Inference Attacks. Numerous existing methods for mitigating these threats have been proposed. However, these methods focus solely on enhancing model security or only on improving model privacy. Ideally, data-driven models should enhance model security and privacy simultaneously. In this paper, we propose methods that combine individual security-enhancing and privacy-enhancing methods to mitigate the security and privacy threats of data-driven models simultaneously. We evaluate the effectiveness of individual security-enhancing methods, individual privacy-enhancing methods, and our methods in simultaneously enhancing model security and privacy. Our comprehensive experimental analysis reveals two-fold insights. First, individual security-enhancing methods can either enhance or diminish model privacy, while individual privacy-enhancing methods face challenges in enhancing model security. Second, our methods improve the effectiveness of simultaneously enhancing model security and privacy compared to the individual methods.