# Privacy-Preserving Biometric Verification with Handwritten Random Digit String

arXiv: https://arxiv.org/abs/2503.12786 · HTML: https://mlsanity.com/paper/2503.12786/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 91 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [Deep Metric Learning with Angular Loss](https://mlsanity.com/paper/1708.01682/) (2017)
- [FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2102.04925/) (2021)
- [DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations](https://mlsanity.com/paper/2009.06308/) (2020)
- [DeepSign: Deep On-Line Signature Verification](https://mlsanity.com/paper/2002.10119/) (2020)
- [BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks](https://mlsanity.com/paper/2001.10223/) (2020)
- [Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence](https://mlsanity.com/paper/2111.09461/) (2021)
- [DeepWriterID: An End-to-end Online Text-independent Writer Identification System](https://mlsanity.com/paper/1508.04945/) (2015)
- [A Perspective Analysis of Handwritten Signature Technology](https://mlsanity.com/paper/2405.13555/) (2024)
- [Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature](https://mlsanity.com/paper/1705.06849/) (2017)
- [MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification](https://mlsanity.com/paper/2210.08836/) (2022)
- [OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification](https://mlsanity.com/paper/1904.00240/) (2019)
- [BioTouchPass: Handwritten Passwords for Touchscreen Biometrics](https://mlsanity.com/paper/2205.01353/) (2022)
- [Letter-level Online Writer Identification](https://mlsanity.com/paper/2112.02824/) (2021)
- [PassBio: Privacy-Preserving User-Centric Biometric Authentication](https://mlsanity.com/paper/1711.04902/) (2017)

## Built on by (most cited first, 4 of 4)

- [OCRGenBench: A Comprehensive Benchmark for Evaluating OCR Generative Capabilities](https://mlsanity.com/paper/2507.15085/) (2025)
- [MSTAR: Box-free Multi-query Scene Text Retrieval with Attention Recycling](https://mlsanity.com/paper/2506.10609/) (2025)
- [Enhancing IMU-Based Online Handwriting Recognition via Contrastive Learning with Zero Inference Overhead](https://mlsanity.com/paper/2602.07049/) (2026)
- [Preserving Privacy Without Compromising Accuracy: Machine Unlearning for Handwritten Text Recognition](https://mlsanity.com/paper/2504.08616/) (2025)

## Abstract (arXiv)

Handwriting verification has stood as a steadfast identity authentication method for decades. However, this technique risks potential privacy breaches due to the inclusion of personal information in handwritten biometrics such as signatures. To address this concern, we propose using the Random Digit String (RDS) for privacy-preserving handwriting verification. This approach allows users to authenticate themselves by writing an arbitrary digit sequence, effectively ensuring privacy protection. To evaluate the effectiveness of RDS, we construct a new HRDS4BV dataset composed of online naturally handwritten RDS. Unlike conventional handwriting, RDS encompasses unconstrained and variable content, posing significant challenges for modeling consistent personal writing style. To surmount this, we propose the Pattern Attentive VErification Network (PAVENet), along with a Discriminative Pattern Mining (DPM) module. DPM adaptively enhances the recognition of consistent and discriminative writing patterns, thus refining handwriting style representation. Through comprehensive evaluations, we scrutinize the applicability of online RDS verification and showcase a pronounced outperformance of our model over existing methods. Furthermore, we discover a noteworthy forgery phenomenon that deviates from prior findings and discuss its positive impact in countering malicious impostor attacks. Substantially, our work underscores the feasibility of privacy-preserving biometric verification and propels the prospects of its broader acceptance and application.