Hierarchical Bloom Filter Framework for Security, Space-efficiency, and Rapid Query Handling in Biometric Systems

Abstract

Incorporating biometric technology into IoT could enable and assist a variety of futuristic applications such as cardless border crossing, secure access control in smart buildings, and patient tracking/anti-fraud in hospitals. However, existing approaches suffer from large storage requirements and latency in query handling. Additionally, there are privacy risks due to security breaches of raw biometric templates. In this paper, we propose a hierarchical bloom filter based identification system for large-scale biometric systems that reduces storage requirements while providing template security and rapid handling of queries. We address the challenge of incorporating a hash-based bloom filter with noisy biometric data by introducing a mathematical framework that is adaptive to characteristics of any biometric database. Our proposed architecture is implemented using a face database containing 30,000 facial templates and achieves 92.05% reduction in storage size with 99.82 reduction in average query time without sacrificing accuracy. Finally, the security is analyzed against potential tampering and collision attacks.