How Anonybit plans to crack honeypots storing identity data

Here’s one way to protect personal and business biometric data so that bad guys can’t find it and make money off it: Don’t store it all in one honeypot – whether it’s a primary or backup database.

Startup Anonybit, which launched on Tuesday and announced a $3.5 million Series A funding round, has developed what it calls a “breakthrough decentralized biometrics infrastructure” that it claims addresses a market need for improved management of personal data and digital assets across a wide range of vertical industries.

This is not a purely SaaS or on-premises security solution. Anonybit dices up sensitive identity data, including biometrics, private keys, and other digital assets, into anonymized bits that are distributed throughout a peer-to-peer network of nodes. The system then applies multi-party computing in a proprietary, patented manner in order to reconnect the bits in a decentralized way. In this way, there is never any identity data for hackers to use for creating false credentials.

[Related: Decentralized identity: The key to the digital era? ]

“Managing identity is central to every digital interaction we have today, and there is no organization that is immune to the challenge,” CEO Frances Zelazny told VentureBeat. “Our approach secures personal data and digital assets, filling a need that banks, fintech, retailers, crypto wallets, government agencies, and other stakeholders for strong authentication without maintaining central honeypots of personal data.”

2021 was a particularly bad one for cybersecurity, with the total number of cyberattack-related data compromises up 27% from 2020. Addressing digital security has been viewed as costly, time-consuming, and complicated, as evidenced by the $1.7 trillion that is expected to be spent over the next five years on cybersecurity and identity management.

On the privacy side, numerous legal frameworks have emerged to address usage and consent issues. However, little has been done to deal with the root cause of the identity problem – central storage of personal data, Zelazny said.

Anonybit, founded in 2018, uses AL and ML in all its processes and offers three products:

• Decentralized identity cloud for biometric solution and identity service providers to use with their algorithms and build privacy-preserving identity solutions
• Turnkey decentralized biometric authentication for enterprises and embedded partners, leveraging state-of-the-art detection, biometric matching, decentralized storage, and integration into orchestration systems;
• Digital asset vault for private keys, backup passphrases, and crypto assets, using the platform’s biometric authentication capabilities to ensure that only the authorized user has access to these assets.

“Anonybit gets to the root of the problem, giving attackers nothing to find and nothing to steal while protecting precious data and assets,” said Switch Ventures’ managing partner Paul Arnold, who led the Series A funding. “Their unique approach to solving the problem is disruptive.”

How the AI is implemented

In order for technologists, data architects, and software developers to learn more about how to utilize AI, VentureBeat asked the following questions of Zelazny, who offered our readers these details:

VentureBeat: What AI and ML tools are you using specifically? 

FZ: We leverage open-source AI and ML biometric models and adapt them in a proprietary manner for Anonybit’s decentralized biometric network.

VentureBeat: Are you using models and algorithms out of a box — for exaFZle, from DataRobot or other sources? 

FZ: We use some out-of-the-box models. For the biometric algorithms, we have our own, but the uniqueness of our platform is that it can support any modality or algorithms. In fact, for our decentralized biometrics cloud offering, we allow biometric solution providers to adapt their algorithm to our infrastructure so they can go to market with a privacy-by-design alternative to their traditional offering.

VentureBeat: What cloud service are you using mainly? 

FZ:  The infrastructure is designed to be cloud-agnostic.

VentureBeat: Are you using a lot of the AI workflow tools that come with that cloud? 

FZ: We leverage many of the workflow tools, but when it comes to biometric processing, we had to develop some of our own.

VentureBeat: How much do you do yourselves? 

FZ: Most of Anonybit’s technologies are home-grown. Today, Anonybit leverages AWS services extensively to build its cloud and ensure its scalability and resilience, but can easily work on Azure or Google Cloud.

VentureBeat: How are you labeling data for the ML and AI workflows? 

FZ: We are using both manual tagging and automation to continuously train our biometric neural network.

VentureBeat: Can you give us a ballpark estimate on how much data you are processing? 

FZ: The Anonybit network is developed with Kubernetes, so it is designed to scale.