Bidirectional Biometric Authentication Using Transciphering and (T)FHE

• URL: http://arxiv.org/abs/2506.12802v1
• Date: Sun, 15 Jun 2025 10:34:57 GMT
• Title: Bidirectional Biometric Authentication Using Transciphering and (T)FHE
• Authors: Joon Soo Yoo, Tae Min Ahn, Ji Won Yoon,
• Abstract summary: Biometric authentication systems pose privacy risks, as leaked templates such as iris or fingerprints can lead to security breaches.<n>We propose the Bidirectional Transciphering Framework (BTF) to enable efficient and privacy-preserving biometric authentication.<n>BTF addresses three core deployment challenges: reducing the size of returned FHE ciphertexts, preventing clients from falsely reporting successful authentication, and enabling scalable, centralized FHE key management.
• Score: 2.423735225769664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Biometric authentication systems pose privacy risks, as leaked templates such as iris or fingerprints can lead to security breaches. Fully Homomorphic Encryption (FHE) enables secure encrypted evaluation, but its deployment is hindered by large ciphertexts, high key overhead, and limited trust models. We propose the Bidirectional Transciphering Framework (BTF), combining FHE, transciphering, and a non-colluding trusted party to enable efficient and privacy-preserving biometric authentication. The key architectural innovation is the introduction of a trusted party that assists in evaluation and key management, along with a double encryption mechanism to preserve the FHE trust model, where client data remains private. BTF addresses three core deployment challenges: reducing the size of returned FHE ciphertexts, preventing clients from falsely reporting successful authentication, and enabling scalable, centralized FHE key management. We implement BTF using TFHE and the Trivium cipher, and evaluate it on iris-based biometric data. Our results show up to a 121$\times$ reduction in transmission size compared to standard FHE models, demonstrating practical scalability and deployment potential.

Related papers

• Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption [0.3683202928838613]
  Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data.<n>The distributed nature of federated learning makes it particularly vulnerable to attacks raised by malicious clients.<n>This paper proposes a privacy-preserving federated learning framework based on verifiable functional encryption.
  arXiv  Detail & Related papers  (2025-06-15T13:38:40Z)
• Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
  Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
  arXiv  Detail & Related papers  (2025-05-26T06:44:31Z)
• Cryptanalysis on Lightweight Verifiable Homomorphic Encryption [7.059472280274008]
  Verifiable Homomorphic Encryption (VHE) is a cryptographic technique that integrates Homomorphic Encryption (HE) with Verifiable Computation (VC)<n>It serves as a crucial technology for ensuring both privacy and integrity in outsourced computation.<n>This paper presents efficient attacks that exploit the homomorphic properties of encryption schemes.
  arXiv  Detail & Related papers  (2025-02-18T08:13:10Z)
• Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
  We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem.<n>These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem.<n>We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
  arXiv  Detail & Related papers  (2025-01-25T04:53:36Z)
• Secure Semantic Communication With Homomorphic Encryption [52.5344514499035]
  This paper explores the feasibility of applying homomorphic encryption to SemCom.<n>We propose a task-oriented SemCom scheme secured through homomorphic encryption.
  arXiv  Detail & Related papers  (2025-01-17T13:26:14Z)
• Cryptanalysis of Cancelable Biometrics Vault [0.552480439325792]
  Cancelable Biometrics (CB) stands for a range of biometric transformation schemes combining biometrics with user specific tokens to generate secure templates.<n>In biometrics, a key-binding scheme is used for protecting a cryptographic key using a biometric data.<n>Our cryptanalysis introduces a new perspective by uncovering the CBV scheme's revocability and linkability vulnerabilities.
  arXiv  Detail & Related papers  (2025-01-10T08:36:59Z)
• Gradient-based facial encoding for key generation to encrypt and decrypt multimedia data [0.873811641236639]
  Security systems relying on passwords are vulnerable to being forgotten, guessed, or breached.<n>This paper introduces a biocryptosystem utilizing face recognition techniques to address these issues.<n>The proposed system creates a distinct 32-bit encryption key derived from facial features.
  arXiv  Detail & Related papers  (2024-12-09T19:12:17Z)
• FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature [60.99054146321459]
  Federated learning allows multiple parties to collaborate in learning a global model without revealing private data. We propose a cryptographic signature-based federated learning model ownership verification scheme named FedSOV.
  arXiv  Detail & Related papers  (2023-05-10T12:10:02Z)
• Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond [57.10914865054868]
  We consider vertical logistic regression (VLR) trained with mini-batch descent gradient. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
  arXiv  Detail & Related papers  (2022-07-19T05:47:30Z)
• THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption [112.02441503951297]
  Privacy-preserving inference of transformer models is on the demand of cloud service users. We introduce $textitTHE-X$, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models.
  arXiv  Detail & Related papers  (2022-06-01T03:49:18Z)
• Security and Privacy Enhanced Gait Authentication with Random Representation Learning and Digital Lockers [3.3549957463189095]
  Gait data captured by inertial sensors have demonstrated promising results on user authentication. Most existing approaches stored the enrolled gait pattern insecurely for matching with the pattern, thus, posed critical security and privacy issues. We present a gait cryptosystem that generates from gait data the random key for user authentication, meanwhile, secures the gait pattern.
  arXiv  Detail & Related papers  (2021-08-05T06:34:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.