Technology

Technology that creates unique, irreversible hashed-tokenized identities and facilitates secure, interoperable identity proofing – anywhere in the world, online and offline

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The Evergreen Hash (EgHash™)

What

Irreversible, hashed-tokenized identities

Why

The effectiveness of all identity data is maximized when it is rendered safe to use, store, and share.

How

Deep neural networks convert any identifying data into a unique, non-PII EgHash

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Security

  • The hashing process is irreversible
  • The EgHash is anonymized data
  • No biometric images or templates are stored

Interoperability

  • The identifying data that is converted into the EgHash can be updated over time
  • The EgHash architecture is compatible with any biometric modality
  • The EgHash can act as a pivot to external data that can be changed to reflect changes in data needs and access

Process

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1. Capture data

In existing use cases, a “secure-selfie” video is taken and tested for liveness

2. Transform data into the EgHash

Data is irreversibly transformed into the EgHash. No semblance of the source data remains

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3. Associate external data via pivot points

Link the anonymized EgHash to any desired data (e.g.: username) to facilitate one-to-one matching for future authentication

The Identity Lake™

What

Secure, interoperable identity proofing

Why

Zero-knowledge authentication provides security beyond that of siloed, legacy authentication systems that are prone to sensitive data leakage and fraud

How

Probabilistic AI predicts whether multiple EgHashes came from the same face, without the need to store or share sensitive information.

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Security

  • Each “identity” in the Lake is meaningless in absence of a matching EgHash. Data that is not there can not be leaked

Interoperability

  • All EgHashes within a modality are comparable regardless of origin
  • Private Lake partners can benefit from shared access while protecting proprietary intelligence by submitting zero-knowledge-proof queries to other Lakes within a Consortium

Process

1. Create EgHash

Irreversibly transform identity data and add external pivot points

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2. Store and de-duplicate in the Lake

Match the EgHash against all others in the Lake to determine fraudulent identities and authenticate existing users

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3. Match against the Consortium

Match the EgHash against other Lakes to maximize risk-prevention and streamline the customer experience across partner entities