What is Zero-Knowledge Biometric Authentication?
A Simple Guide for Security Teams

Biometric authentication is now part of everyday digital life. But more often than not, biometric systems force security, fraud, product, and digital transformation teams to make trade-offs between security, privacy, and usability - with cost and integration also factored in.

Zero-Knowledge Biometrics™ (ZKB) is a novel approach to authentication developed by Keyless (now part of Ping Identity) that aims to put and end to having to choose. By combining advanced cryptography with biometrics, ZKB allows organisations to deliver strong, user-friendly authentication without compromising user privacy.

In this guide, we’ll explain what Zero-Knowledge Biometrics is, how it works, and why organizations are using it to offer a more secure, private, and user-friendly alternative to conventional authentication models.

Before diving into Zero-Knowledge Biometrics, it's important to understand the three common models for biometric authentication:

These systems store and verify biometric data directly on the user’s device. Examples include FaceID and Android Biometrics.

• High privacy, as data never leaves the device

• No cross-platform or cross-device usability

• No visibility or control for service providers

• Can be bypassed if someone knows the device passcode

In centralized systems, biometric templates are stored and authenticated on a central server, usually in the cloud.

• Supports cross-device and cross-platform use

• Offers consistent account-level authentication

• Creates a centralized PII honeypot and poses a serious privacy and compliance risk if breached

• Often incompatible with regulations like GDPR

Decentralized systems try to avoid storing biometric data in one place. According to its 2025 Innovation Insight on Biometric Authentication, Gartner defines decentralized systems as biometric authentication systems that are neither local nor centralized. This approach is still nascent and its exact definition within the biometric authentication space is yet to be fully agreed on.

Most commercial implementations of decentralized biometrics rely on a technique known as sharding. In this approach, the user's biometric data is split into fragments, or "shares," and distributed across multiple servers. During authentication, each server compares its share against a portion of the incoming biometric sample.

The logic behind this is that because no single server holds the entire biometric template, user data is better protected. However, the reality is more complicated. In many cases, the vendor controls all or most of the servers, meaning the system is still vulnerable to compromise. If just a subset of the servers is breached, an attacker may be able to reconstruct the user's biometric profile or perform de-anonymization attacks using publicly available photos. Even a single share can be risky, as partial biometric data can sometimes be used to identify individuals.

From a compliance perspective, these systems also raise red flags. Under GDPR, any data that can be used to reconstruct or link back to a user’s biometric identity is considered biometric data. This includes encrypted templates and distributed shares. As a result, these solutions often fall short of the regulatory protections they aim to satisfy.

To summarize, with regarding to sharding:

• In practice, many vendors still control all the servers

• If one server is compromised, partial matches can still leak sensitive data

• Often fail to meet the privacy expectations they promise

Zero-Knowledge Biometrics is a new approach to decentralized biometric authentication that avoids storing, sharing, or reconstructing biometric data. Rather than relying on sharding, it uses secure Multi-Party Computation (sMPC) to verify a user’s identity without revealing their biometric data to any party.

It’s called “zero-knowledge” because neither the user device nor the server learns anything about the actual biometric data - only whether the submitted sample matches the enrolled profile. This significantly enhances both privacy and security.

Unlike traditional decentralized models that distribute fragments of biometric data across servers (and often still allow reconstruction), Zero-Knowledge Biometrics never exposes or stores the data in a way that makes reconstruction of the data or image possible. The biometric is transformed into a cryptographic format at the point of capture, and that format is what is used throughout the authentication process.

Understanding how sMPC works is easiest through the Millionaires’ Problem in cryptography. Two people want to know who is richer, but neither wants to disclose their actual wealth. Secure Multi-Party Computation solves this by allowing them to compute the answer - who is richer - without revealing any personal financial details. The process produces only the result, not the inputs.

Zero-Knowledge Biometrics applies this same principle to facial authentication:

During Enrollment

• The user takes a selfie or facial scan

• It’s transformed locally into an unrecognizable cryptographic format

• This transformed template is stored in the cloud, not the raw image

During Authentication

• The user takes a fresh selfie

• That image is again transformed locally

• The transformed template is compared to the stored cryptographic proof using sMPC

• No raw data is ever reconstructed or exposed

The entire process happens in less than 300 milliseconds, with no user friction and no data leakage - at rest, in transit, or in use.

The end result is that Zero-Knowledge Biometrics offers the security and scalability of centralized systems, with the privacy of local biometrics - without inheriting the downsides of either. It works across platforms and devices, supports seamless recovery if a device is lost, and maintains full compliance with privacy regulations like GDPR.

Zero-Knowledge Biometrics supports a wide range of deployment models:

• Cloud: for fast and scalable rollouts

• On-premise: for regulated environments needing internal control

• Hybrid: keeping sensitive data local while leveraging cloud performance

• SDKs and APIs: easily integrated into mobile apps, web platforms, and workforce systems

Security teams shouldn’t have to choose between security, privacy, and usability. Zero-Knowledge Biometrics proves that you can have all three - plus fast integration, regulatory compliance, and long-term cost efficiency.