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Privacy-Preserving Biometric Verification With Handwritten Random Digit String
Summary
Privacy concerns in handwriting verification are addressed using Random Digit Strings (RDS). Our new model, PAVENet with DPM, effectively verifies handwriting while protecting personal information.
Area of Science:
- Biometrics
- Computer Science
- Cybersecurity
Background:
- Traditional handwriting verification methods pose privacy risks due to personal data in signatures.
- Biometric authentication requires robust privacy-preserving solutions.
Purpose of the Study:
- To introduce Random Digit String (RDS) as a privacy-preserving method for handwriting verification.
- To develop and evaluate a novel deep learning model for RDS-based authentication.
Main Methods:
- Construction of the HRDS4BV dataset for online handwritten RDS.
- Proposal of the Pattern Attentive VErification Network (PAVENet) with Discriminative Pattern Mining (DPM).
- DPM module enhances recognition of consistent and discriminative writing patterns for improved style representation.
Main Results:
- PAVENet demonstrated superior performance over existing methods in online RDS verification.
- A novel forgery phenomenon was identified, offering enhanced defense against impostor attacks.
- The study validates the effectiveness of RDS for privacy-preserving biometric verification.
Conclusions:
- Random Digit String (RDS) offers a viable and private alternative for handwriting verification.
- PAVENet and DPM significantly advance the accuracy and privacy of biometric authentication.
- This research paves the way for wider adoption of privacy-preserving biometric technologies.
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