Blind-Touch: Homomorphic Encryption-Based Distributed Neural Network Inference for Privacy-Preserving Fingerprint Authentication

• URL: http://arxiv.org/abs/2312.11575v2
• Date: Mon, 1 Apr 2024 13:15:29 GMT
• Title: Blind-Touch: Homomorphic Encryption-Based Distributed Neural Network Inference for Privacy-Preserving Fingerprint Authentication
• Authors: Hyunmin Choi, Simon Woo, Hyoungshick Kim,
• Abstract summary: Homomorphic encryption allows computations encrypted on data without decrypting. Blind-Touch can keep fingerprint data encrypted on the server while performing machine learning operations. Blind-Touch achieves high accuracy on two benchmark fingerprint datasets.
• Score: 8.61984933438984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fingerprint authentication is a popular security mechanism for smartphones and laptops. However, its adoption in web and cloud environments has been limited due to privacy concerns over storing and processing biometric data on servers. This paper introduces Blind-Touch, a novel machine learning-based fingerprint authentication system leveraging homomorphic encryption to address these privacy concerns. Homomorphic encryption allows computations on encrypted data without decrypting. Thus, Blind-Touch can keep fingerprint data encrypted on the server while performing machine learning operations. Blind-Touch combines three strategies to efficiently utilize homomorphic encryption in machine learning: (1) It optimizes the feature vector for a distributed architecture, processing the first fully connected layer (FC-16) in plaintext on the client side and the subsequent layer (FC-1) post-encryption on the server, thereby minimizing encrypted computations; (2) It employs a homomorphic encryption compatible data compression technique capable of handling 8,192 authentication results concurrently; and (3) It utilizes a clustered server architecture to simultaneously process authentication results, thereby enhancing scalability with increasing user numbers. Blind-Touch achieves high accuracy on two benchmark fingerprint datasets, with a 93.6% F1- score for the PolyU dataset and a 98.2% F1-score for the SOKOTO dataset. Moreover, Blind-Touch can match a fingerprint among 5,000 in about 0.65 seconds. With its privacy focused design, high accuracy, and efficiency, Blind-Touch is a promising alternative to conventional fingerprint authentication for web and cloud applications.

Related papers

• Gait-Based Privacy Protection for Smart Wearable Devices [7.358293252870294]
  Existing gait identification methods have low accuracy and neglect to protect the privacy of gait features. A biometric-based encryption scheme is proposed to enable secure message interactions using gait features as keys after the gait identification stage is passed.
  arXiv  Detail & Related papers  (2024-02-24T12:05:42Z)
• I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption [5.12893315783096]
  We introduce BlindTuner, a privacy-preserving fine-tuning system that enables transformer training exclusively on homomorphically encrypted data for image classification. Our findings highlight a substantial speed enhancement of 1.5x to 600x over previous work in this domain.
  arXiv  Detail & Related papers  (2024-02-14T10:15:43Z)
• dabih -- encrypted data storage and sharing platform [0.0]
  dabih is an open-source web application designed to facilitate user-friendly encrypted data management. Its approach to data security involves a two-stage envelope encryption process. The private key necessary for decrypting the data remains exclusively on the owner's device.
  arXiv  Detail & Related papers  (2024-01-16T12:57:35Z)
• SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
  We propose SOCI+ which significantly improves the performance of SOCI. SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
  arXiv  Detail & Related papers  (2023-09-27T05:19:32Z)
• Conditional Generative Adversarial Network for keystroke presentation attack [0.0]
  We propose to study a new approach aiming to deploy a presentation attack towards a keystroke authentication system. Our idea is to use Conditional Generative Adversarial Networks (cGAN) for generating synthetic keystroke data that can be used for impersonating an authorized user. Results indicate that the cGAN can effectively generate keystroke dynamics patterns that can be used for deceiving keystroke authentication systems.
  arXiv  Detail & Related papers  (2022-12-16T12:45:16Z)
• Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning [63.45532264721498]
  Self-supervised learning is an emerging technique to pre-train encoders using unlabeled data. We perform first systematic, principled measurement study to understand whether and when a pretrained encoder can address the limitations of secure or privacy-preserving supervised learning algorithms.
  arXiv  Detail & Related papers  (2022-12-06T21:35:35Z)
• AFR-Net: Attention-Driven Fingerprint Recognition Network [47.87570819350573]
  We improve initial studies on the use of vision transformers (ViT) for biometric recognition, including fingerprint recognition. We propose a realignment strategy using local embeddings extracted from intermediate feature maps within the networks to refine the global embeddings in low certainty situations. This strategy can be applied as a wrapper to any existing deep learning network (including attention-based, CNN-based, or both) to boost its performance.
  arXiv  Detail & Related papers  (2022-11-25T05:10:39Z)
• 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)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)
• TypeNet: Deep Learning Keystroke Biometrics [77.80092630558305]
  We introduce TypeNet, a Recurrent Neural Network trained with a moderate number of keystrokes per identity. With 5 gallery sequences and test sequences of length 50, TypeNet achieves state-of-the-art keystroke biometric authentication performance. Our experiments demonstrate a moderate increase in error with up to 100,000 subjects, demonstrating the potential of TypeNet to operate at an Internet scale.
  arXiv  Detail & Related papers  (2021-01-14T12:49:09Z)
• Scalable Data Classification for Security and Privacy [0.06445605125467573]
  This paper is about an end-to-end system built to detect sensitive semantic types within Facebook at scale. The described system is in production achieving a 0.9+ average F2 scores across various privacy classes.
  arXiv  Detail & Related papers  (2020-06-25T00:19:34Z)

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.