ZK-SERIES: Privacy-Preserving Authentication using Temporal Biometric Data

2025-06-24

AI Summary

To address the dual challenges of insufficient privacy protection and high computational overhead in time-series behavioral biometric authentication, this paper proposes ZK-SERIES—the first unified zero-knowledge proof (ZKP) framework tailored for behavioral biometrics (e.g., shaking, blowing). ZK-SERIES integrates sequence-matching protocols—including Dynamic Time Warping (DTW) and Time-Warp Edit Distance (TWED)—into a ZKP system, innovatively combining zero-knowledge multiplication proofs with batched range proofs to enable multi-protocol compatibility and efficient batch verification. Privacy-wise, it provably hides raw time-series data and matching intermediate values. Efficiency-wise, end-to-end authentication completes in just 1.3 seconds on a five-year-old low-end smartphone. Extensive experiments—conducted on both real-world and synthetic datasets—demonstrate ZK-SERIES’s scalability, practicality, and strong privacy guarantees under standard cryptographic assumptions.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Biometrics, Face, Gesture & Pose
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Biometric authentication relies on physiological or behavioral traits that are inherent to a user, making them difficult to lose, forge or forget. Biometric data with a temporal component enable the following authentication protocol: recent readings of the underlying biometrics are encoded as time series and compared to a set of base readings. If the distance between the new readings and the base readings falls within an acceptable threshold, then the user is successfully authenticated. Various methods exist for comparing time series data, such as Dynamic Time Warping (DTW) and the Time Warp Edit Distance (TWED), each offering advantages and drawbacks depending on the context. Moreover, many of these techniques do not inherently preserve privacy, which is a critical consideration in biometric authentication due to the complexity of resetting biometric credentials. In this work, we propose ZK-SERIES to provide privacy and efficiency to a broad spectrum of time series-based authentication protocols. ZK-SERIES uses the same building blocks, i.e., zero-knowledge multiplication proofs and efficiently batched range proofs, to ensure consistency across all protocols. Furthermore, it is optimized for compatibility with low-capacity devices such as smartphones. To assess the effectiveness of our proposed technique, we primarily focus on two case studies for biometric authentication: shake-based and blow-based authentication. To demonstrate ZK-SERIES's practical applicability even in older and less powerful smartphones, we conduct experiments on a 5-year-old low-spec smartphone using real data for two case studies alongside scalability assessments using artificial data. Our experimental results indicate that the privacy-preserving authentication protocol can be completed within 1.3 seconds on older devices.

Problem

• Enhance privacy in biometric authentication using temporal data
• Optimize time series comparison for low-capacity devices
• Ensure efficient and secure authentication with zero-knowledge proofs

Innovation

• Uses zero-knowledge proofs for privacy
• Optimized for low-capacity smartphones
• Supports temporal biometric authentication