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Biometric fusion is a promising method to elevate the recognition performance of unimodal biometric systems. Nevertheless, the exposure of feature vectors for feature-level fusion raises security concerns, as it is feasible to extract sensitive information from these vectors. This paper proposes a non-interactive, end-to-end approach to securely fuse and match biometric templates using Fully Homomorphic Encryption (FHE). For a pair of encrypted feature vectors, we perform the following operations on a ciphertext domain: i) feature concatenation, ii) fusion and dimensionality reduction through a learned linear projection, iii) an optional scale normalization to unit 2 -norm, and iv) match score computation. Our method, dubbed HEFT, is custom-designed to circumvent a key limitation of FHE - the lack of support for non-arithmetic operations. From an inference perspective, we systematically explore different data packing schemes for computationally efficient linear projection and introduce a polynomial approximation for scale normalization. From a training perspective, we introduce two distinct FHE-aware algorithms to improve the learning of the projection matrix and address the challenges posed by the nonarithmetic normalization step. We demonstrate the utility of HEFT on two multimodal combinations: face and voice and face and fingerprint. For the face-voice fusion, HEFT improves verification performance by a range of 143.25% - 244.35% compared to unibiometric features. On the fingerprint-face fusion, improvements are from 13.99% to 37.99%. Code and data are available at https://github.com/human-analysis/encryptedbiometric-fusionhttps://github.com/human-analysis/encryptedbiometric-fusion.
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This content will become publicly available on April 1, 2027
SoK: Can Fully Homomorphic Encryption Support General AI Computation? A Functional and Cost Analysis
Artificial intelligence (AI) increasingly powers sensitive applications in domains such as healthcare and finance, relying on both extit{linear operations} (e.g., matrix multiplications in large language models) and extit{non-linear operations} (e.g., sorting in retrieval-augmented generation). Fully homomorphic encryption (FHE) has emerged as a promising tool for privacy-preserving computation, but it remains unclear whether existing methods can support the full spectrum of AI workloads that combine these operations. In this SoK, we ask: extit{Can FHE support general AI computation?} We provide both a functional analysis and a cost analysis. First, we categorize ten distinct FHE approaches and evaluate their ability to support general computation. We then identify three promising candidates and benchmark workloads that mix linear and non-linear operations across different bit lengths and SIMD parallelization settings. Finally, we evaluate five real-world, privacy-sensitive AI applications that instantiate these workloads. Our results quantify the costs of achieving general computation in FHE and offer practical guidance on selecting FHE methods that best fit specific AI application requirements. Our codes are available at https://github.com/UCF-ML-Research/FHE-AI-Generality.
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- PAR ID:
- 10704288
- Publisher / Repository:
- PETS
- Date Published:
- Journal Name:
- Proceedings on Privacy Enhancing Technologies
- Volume:
- 2026
- Issue:
- 2
- ISSN:
- 2299-0984
- Page Range / eLocation ID:
- 680 to 696
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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