Biometrics are fragmented and dispersed across a network without reassembly for matching
Decentralized biometrics specialist Anonybit has achieved an industry first by adding 1:N (one-to-many) biometric identification capabilities to its decentralized biometrics cloud service. The product demonstrates that even large-scale deployments such as government ID applications can be safely decentralized.
This privacy-by-design addition means that individuals’ biometrics are split into anonymized bits that are distributed over a large network, encrypted, and stored ready for future matching. When verification is required, these scattered biometric fragments do not need to be reassembled, eliminating the risk of a data breach according to the New York-based firm.
The system enables decentralized deduplication at the point of enrollment and search capabilities, which means the company can provide privacy-preserving services in many use cases that require 1:N rather than a simple 1 match. :1 simpler.
The service will be available as an add-on to Anonybit’s turnkey decentralized authentication product and will be available to other biometric solution providers, allowing them to use a decentralized cloud that is GDPR and California CPRA compliant. .
Face, fingerprint, iris, and voice biometric modalities and algorithms are all supported for third-party biometric providers.
The product also supports liveness detection, decentralized storage, and 1:1 matching.
“Our goal is to cut off the oxygen that fuels identity thieves by giving them nothing to find and nothing to steal, even if they manage to break into a network,” comments Frances Zelazny, co-founder and CEO of Anonymit.
“By eliminating central honeypots while enabling strong biometric authentication, we are ushering in a new era for privacy and security, enabled by our innovative decentralized biometric infrastructure. One-to-many capability is the cornerstone of creating this future, enabling governments, businesses and other entities to ensure priority registration for individuals based on their biometrics. This creates a trust anchor that supports all other types of deployments, such as verifiable credentials, online account access, time and attendance systems, payments, and others.
Zelazny explained Anonybit’s ambitions and the potential benefits of its storage architecture for biometric identification and deduplication processes, in an interview with Biometric update Last year.
Anonybit recently partnered with facial biometrics developers Aware to create secure biometric templates for government use, and FacePhi to integrate decentralized storage into a smart city project. This follows a $3.5 million funding round to expand storage of sensitive data so biometrics vendors don’t have to.
Anonymous | anonymization | biometric data | biometric identification | biometric matching | biometrics | cloud services | data protection | data storage | decentralized biometrics | Privacy by Design