# AMB-FHE: Adaptive Multi-biometric Fusion with Fully Homomorphic Encryption

arXiv: https://arxiv.org/abs/2503.23949 · HTML: https://mlsanity.com/paper/2503.23949/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Secure Face Matching Using Fully Homomorphic Encryption](https://mlsanity.com/paper/1805.00577/) (2018)
- [Benchmarking fixed-length Fingerprint Representations across different Embedding Sizes and Sensor Types](https://mlsanity.com/paper/2307.08615/) (2023)
- [HEFT: Homomorphically Encrypted Fusion of Biometric Templates](https://mlsanity.com/paper/2208.07241/) (2022)
- [Benchmarking of Cancelable Biometrics for Deep Templates](https://mlsanity.com/paper/2302.13286/) (2023)
- [Securing Biometric Data: Fully Homomorphic Encryption in Multimodal Iris and Face Recognition](https://mlsanity.com/paper/2408.14609/) (2024)
- [Fast Matching by 2 Lines of Code for Large Scale Face Recognition Systems](https://mlsanity.com/paper/1302.7180/) (2013)
- [Multi-Biometric Fuzzy Vault based on Face and Fingerprints](https://mlsanity.com/paper/2301.06882/) (2023)

## Built on by (most cited first, 1 of 1)

- [Training-free Dimensionality Reduction via Feature Truncation: Enhancing Efficiency in Privacy-preserving Multi-Biometric Systems](https://mlsanity.com/paper/2508.11419/) (2025)

## Abstract (arXiv)

Biometric systems strive to balance security and usability. The use of multi-biometric systems combining multiple biometric modalities is usually recommended for high-security applications. However, the presentation of multiple biometric modalities can impair the user-friendliness of the overall system and might not be necessary in all cases. In this work, we present a simple but flexible approach to increase the privacy protection of homomorphically encrypted multi-biometric reference templates while enabling adaptation to security requirements at run-time: An adaptive multi-biometric fusion with fully homomorphic encryption (AMB-FHE). AMB-FHE is benchmarked against a bimodal biometric database consisting of the CASIA iris and MCYT fingerprint datasets using deep neural networks for feature extraction. Our contribution is easy to implement and increases the flexibility of biometric authentication while offering increased privacy protection through joint encryption of templates from multiple modalities.