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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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IT2

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 IT2

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Security

  • The hashing process is irreversible
  • The IT2 is pseudonymized data
  • No biometric images or templates are stored

Interoperability

  • The identifying data that is converted into the IT2 can be updated over time
  • The IT2 architecture is compatible with any biometric modality
  • The IT2 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

Data is irreversibly transformed with no biometric templates stored, thus achieving Trusted Presence™.

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3. Associate external data via pivot points

Link the pseudonymized IT2 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 IT2 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 IT2. Data that is not there can not be leaked

Interoperability

  • All IT2 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 IT2

Irreversibly transform identity data and add external pivot points

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2. Store and de-duplicate in the Lake

Match the IT2 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 IT2 against other Lakes to maximize risk-prevention and streamline the customer experience across partner entities

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