Blockchain optimized for big data and AI, with U.S. DoD cybersecurity accredited, zero trust architecture
zk-STARK implementation of energy and identity zero-knowledge proofs
Application to sign blockchain transactions with SSO and enterprise RBAC and IAM
Encrypted cloud-agnostic storage with zero-trust access and private deployments
Quantum-resistant SHA-384 hashing to fingerprint arbitrary data types. Advanced image file verification and integrity tested at scale by U.S. intelligence agency
Smart contracts to automate the compensation of individuals and organizations for their data and / or their participation in collective action programs (e.g., energy efficiency, transportation, logistics)
24/7, natural language, multilingual technical support service to help customers use platform components
Blockchain-based approach to integrate and analyze (e.g., AI) decentralized private data securely (14+ patents issued by the USPTO)
* Formerly known as Trusted Analytics Chain™ (TAC™)
Automation to ingest diverse raw data files into an AI-ready format in minutes
“The DoD believes that Web 3.0 Blockchain technology can be leveraged to build systems that focus on protecting the data independently of the systems that store, transport, and process it through decentralization.”
SOLUTION BRIEF
Learn how Web3 Multi-Party Computation (formerly known as Trusted Analytics Chain™) enables secure control of any combination of data sources and analysis among multiple collaborators.
Last updated: May 2023
WHITE PAPER
Learn how Web3 Multi-Party Computation (formerly known as Trusted Analytics Chain™) enables the analysis of sensitive data.
Last updated: May 2023
SOLUTION BRIEF
Learn about VIAsecurechain, the first and only U.S. DoD cybersecurity accredited blockchain.
Last updated: August 2024
John is focused on implementing best practices for software development at VIA. He co-invented VIA’s core platform and currently develops, integrates, and quality assurance tests the platform for VIA’s applications.
Before VIA, John was a freelance consultant at the Numerical Algorithms Group (NAG), where he focused on updating their Python Library. Prior to this position, he was a technical sales support engineer at NAG in Oxford, where he developed technical marketing material and provided pre-sales assistance. John was awarded the best thesis in gravitation physics in 2016 by the Institute of Physics (IOP). His research focused on developing new numerical methods that capture the physical behavior of fluid interfaces permeated by high magnetic fields while taking into account relativistic effects.
John earned his master’s degree in physics and his Ph.D. in applied mathematics from the University of Southampton.