An Adversarial Machine Learning Based Approach for Privacy Preserving Face Recognition in Distributed Smart City Surveillance

Abstract

Smart cities rely heavily on surveillance cameras for urban management and security. However, the extensive use of these cameras also raises significant concerns regarding data privacy. Unauthorized access to facial data captured by these cameras and the potential for misuse of this data poses serious threats to individuals’ privacy. Current privacy preservation solutions often compromise data usability with noise application-based approaches and vulnerable centralized data handling settings. To address these privacy challenges, we propose a novel approach that combines Adversarial Machine Learning (AML) with Federated Learning (FL). Our approach involves the use of a noise generator that perturbs surveillance data right from the source before they leave the surveillance cameras. By exclusively training the Federated Learning model on these perturbed samples, we ensure that sensitive biometric features are not shared with centralized servers. Instead, such data remains on local devices (e.g., cameras), thereby ensuring that data privacy is maintained. We performed a thorough real-world evaluation of the proposed method and achieved an accuracy of around 99.95% in standard machine learning settings. In distributed settings, we achieved an accuracy of around 96.24% using federated learning, demonstrating the practicality and effectiveness of the proposed solution.¹