Secure and Scalable Face Retrieval via Cancelable Product Quantization
This addresses privacy concerns for users of face retrieval systems, but it is incremental as it builds on existing quantization and encryption methods.
The paper tackles the problem of privacy risks in outsourced face retrieval systems by proposing Cancelable Product Quantization, a framework that balances security and efficiency, achieving decent performance on benchmark datasets.
Despite the ubiquity of modern face retrieval systems, their retrieval stage is often outsourced to third-party entities, posing significant risks to user portrait privacy. Although homomorphic encryption (HE) offers strong security guarantees by enabling arithmetic computations in the cipher space, its high computational inefficiency makes it unsuitable for real-time, real-world applications. To address this issue, we propose Cancelable Product Quantization, a highly efficient framework for secure face representation retrieval. Our hierarchical two-stage framework comprises: (i) a high-throughput cancelable PQ indexing module for fast candidate filtering, and (ii) a fine-grained cipher-space retrieval module for final precise face ranking. A tailored protection mechanism is designed to secure the indexing module for cancelable biometric authentication while ensuring efficiency. Experiments on benchmark datasets demonstrate that our method achieves an decent balance between effectiveness, efficiency and security.