# Secure and Scalable Face Retrieval via Cancelable Product Quantization

arXiv: https://arxiv.org/abs/2509.00781 · HTML: https://mlsanity.com/paper/2509.00781/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 31 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [On the Reconstruction of Face Images from Deep Face Templates](https://mlsanity.com/paper/1703.00832/) (2017)
- [Secure Face Matching Using Fully Homomorphic Encryption](https://mlsanity.com/paper/1805.00577/) (2018)
- [HERS: Homomorphically Encrypted Representation Search](https://mlsanity.com/paper/2003.12197/) (2020)
- [Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing](https://mlsanity.com/paper/1703.05455/) (2017)
- [Towards Protecting Face Embeddings in Mobile Face Verification Scenarios](https://mlsanity.com/paper/2110.00434/) (2021)
- [Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques](https://mlsanity.com/paper/2110.05044/) (2021)
- [Privacy-Preserving Content-Based Image Retrieval in the Cloud](https://mlsanity.com/paper/1411.4862/) (2014)

## Abstract (arXiv)

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.