Anonymous subject identification in privacy-aware video surveillance

The widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been recently proposed to identify selected individuals and redact their images in the surveillance video. To identify individuals, the best known approach is to use biometric signals as they are immutable and highly discriminative. If misused, these characteristics of biometrics can seriously defeat the goal of privacy protection. In this paper, we propose an anonymous subject identification system based on homomorphic encryption (HE). It matches the biometric signals in encrypted domain to provide anonymity to users. To make the HE-based protocols computationally scalable, we propose a complexity-privacy tradeoff called k-Anonymous Quantization (kAQ) which narrows the plaintext search to a small cell before running the intensive encrypted-domain processing within the cell. We validate a key assumption in kAQ that privacy is better preserved by grouping biometric patterns far apart into the same cell. We also improve the matching success rate by replacing the original bounding boxes with ε-balls as basic units for grouping. Experimental results on a public iris biometric database demonstrate the validity of our framework.