A Privacy Protection Scheme for Facial Recognition and Resolution Based on Edge Computing

Abstract

Facial recognition and resolution technology have extensive application scenarios in the era of big data. It ensures the consistency of personal identity in physical space and cyberspace by establishing correspondence between physical objects and network entities. However, massive data brings huge processing pressure to cloud service, and there are data leakage risks about personal information. To address this problem, we propose a privacy security protection scheme for facial recognition and resolution based on edge computing. Firstly, a facial recognition and resolution framework based on edge computing is established, which improves the communication and storage efficiency through task partition and relieves the pressure of cloud computing. Then, a verifiable deletion scheme based on Hidden CP-ABE is proposed to provide fine-grained access control and ensure the safe deletion of target data in the cloud. Moreover, after applying the verifiable deletion method, the safe deletion of the target data in the cloud can be achieved. Finally, the simulation results show the effectiveness and security of the proposed scheme.

Integration of Artificial Intelligence in Facial Recognition Systems for Software Security

“…As noted (Patel et al, 2020) issues such as data quality, system interoperability, and real-time processing demands require innovative solutions. Researchers have proposed various methods to address these challenges, including the use of hybrid cloud systems that enhance data processing capabilities and the implementation of edge computing to reduce latency in facial recognition operations (Aldi, 2024;Rathour et al, 2021;Udayana et al, 2022;Wu et al, 2022;H. Zhang et al, 2019).…”