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Here’s one way to protect personal and business biometric data so bad guys can’t find it and make money: don’t store everything in a honeypot, whether it’s a master database or backup.
Startup Anonybit, which launched on Tuesday and announced a $3.5 million Series A funding round, has developed what it calls a “breakthrough decentralized biometric infrastructure” that it says fills a need market for improved management of personal data and digital assets in a wide range of areas. vertical industries.
It is not a purely SaaS or on-premises security solution. Anonybit chops sensitive identity data, including biometrics, private keys, and other digital assets, into anonymized bits that are distributed over a network of peer-to-peer nodes. The system then applies proprietary and patented multiparty computing to reconnect the bits in a decentralized way. This way, there is never any identity data that hackers can use to create fake credentials.
[Related: Decentralized identity: The key to the digital era? ]
“Identity management is at the heart of every digital interaction we have today, and no organization is immune to this challenge,” CEO Frances Zelazny told VentureBeat. “Our approach secures personal data and digital assets, addressing a need of banks, fintechs, retailers, crypto wallets, government agencies and other stakeholders for strong authentication without maintaining central honeypots of personal data.”
2021 was a particularly bad year for cybersecurity, with the total number of data breaches related to cyberattacks increasing by 27% compared to 2020. Digital security was considered expensive, time-consuming and complicated, as evidenced by the 1 $700 billion expected. to devote over the next five years to cybersecurity and identity management.
On the privacy side, many legal frameworks have emerged to address issues of use and consent. However, little has been done to address the root cause of the identity problem – the 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 biometrics and identity service providers to use with their algorithms and build privacy-preserving identity solutions
- Turnkey decentralized biometric authentication for enterprises and integrated partners, leveraging advanced sensing, biometric matching, decentralized storage, and integration into orchestration systems;
- Digital Asset Vault for private keys, backup passphrases and cryptographic 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 valuable data and assets,” said Paul Arnold, Managing Partner of Switch Ventures, which led the Series A funding. “Their unique approach to solving the problem is disruptive.”
How AI is implemented
In order for technologists, data architects, and software developers to learn more about using AI, VentureBeat posed the following questions to Zelazny, who provided these details for our readers:
VentureBeat: What AI and ML tools do you specifically use?
FZ: We leverage open source AI and ML biometric models and adapt them exclusively to Anonybit’s decentralized biometric network.
VentureBeat: Do you use off-the-shelf models and algorithms — by exaFZle, from DataRobot, or other sources?
FZ: We use ready-to-use templates. For biometric algorithms we have our own, but the particularity of our platform is that it can support any modality or algorithm. In fact, for our decentralized biometric cloud offering, we enable biometric solution providers to adapt their algorithm to our infrastructure so that they can market a privacy-by-design alternative to their traditional offering.
VentureBeat: What cloud service do you mainly use?
FZ: The infrastructure is designed to be independent of the cloud.
VentureBeat: Do you use a lot of the AI workflow tools that come with this cloud?
FZ: We leverage many workflow tools, but when it comes to biometric processing, we had to develop some of our own.
VentureBeat: How much do you make yourself?
FZ: Most of Anonybit’s technologies are developed in-house. Today, Anonybit leverages AWS services extensively to build its cloud and ensure scalability and resilience, but can easily run on Azure or Google Cloud.
VentureBeat: How do you label data for ML and AI workflows?
FZ: We use both manual tagging and automation to continuously train our biometric neural network.
VentureBeat: Can you give us a rough estimate of how much data you process?
FZ: The Anonybit network is developed with Kubernetes, so it’s designed to scale.
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