An Efficient Privacy-preserving IoT System for Face Recognition

Abstract

Face recognition (FR) has become increasingly important in many real-world IoT applications like public safety surveillance camera system or CCTV, person re-identification system and face-based authentication system. The privacy of face images has been a growing concern, not only in the collected face dataset stored in the cloud platforms but also in its everyday use. However, most existing schemes (e.g., deep learning with differential privacy [1]) build privacy-preserving analytics models from the stored face data while ignoring the privacy concern in end devices. In this paper, we propose a novel efficient privacy-preserving face representation scheme in the Bloom filter space, which can satisfy the resource limits from IoT devices. Our solution allows analytics tasks on privacy-preserving face data representation but retains the high data utility on analytics (e.g., classification).