4 minute read


  • Introducing the MATTR Platform

    Here at MATTR, we have been hard at work building a suite of products to serve the next generation of digital trust. We’ve designed our products based on a few key principles: extensible data formats, secure authentication protocols, a rigorous semantic data model, industry-standard cryptography, and the use of drivers and extensions to allow modular and configurable use of the platform over time. By combining our core capabilities with extensions and drivers, our platform offers developers convenience without compromising flexibility or choice.

  • SSI? What we really need is full data portability

    Governments and other stakeholders exploring SSI are less interested in ideology and more interested in improving the user experience for their customers and constituents. They want to increase access to services, improve service delivery, and safely digitalize interactions, while mitigating privacy and data security-related risks. The key to these objectives lies in full data portability—this means granting individuals robust legal rights, as well as straightforward technical tools and capabilities, to manage and use identity credentials and other personal data with more trust, confidence and ease, so that they can share medical records with a new doctor, port professional credentials to a new employer, and the like.

  • A solution for privacy-preserving Verifiable Credentials

    Here at MATTR, we are piloting an approach to ZKPs based on BBS+ signatures. Beyond the privacy and security benefits of ZKPs in general, this approach has a number of additional benefits compared to the ZKP implementations that exist today.

  • JWT vs Linked Data Proofs: comparing Verifiable Credentials

    Linked Data Proofs offer more flexibility and are thus more scalable for global decentralized networks. Plus, because they natively work with JSON-LD, they encourage adoption of an open-world data model and re-usage of schemas that makes JSON-LD so powerful.

    JWTs, in contrast, offer a simple and straightforward way to express data with a limited semantic vocabulary. Using JWTs with JSON-LD provides a potential compromise between the two approaches, but loses much of the flexibility provided by Linked Data Security.

  • Using privacy-preserving ZKP credentials on the MATTR Platform

    By leveraging pairing-friendly elliptic-curve cryptography in the context of Linked Data Proofs, our approach provides an unprecedented way to perform zero-knowledge proofs using the semantics of JSON-LD. This allows credential issuers to tap into vast data vocabularies that exist on the web today, such as and Google Knowledge Graph, making user data more context-rich without sacrificing security and privacy of the user in the process. Not only is this approach more interoperable with existing implementations of the VC data model and semantic web technologies, it also doesn’t rely on any external dependencies to operate (like a distributed ledger), meaning it’s far more efficient than other approaches based on CL-signatures and zk-SNARKs. We’ve open-sourced our LD-Proofs suite for VCs including performance benchmarks so you can check it out yourself.

  • DID Extensibility on the MATTR Platform

    DID Web helps to bridge the gap between the way that trust is established on the internet today, namely using domains, and new and emerging ecosystems using DIDs. When using DID Web, rather than anchoring a DID to a decentralized ledger such as a blockchain, the DID is instead associated with a specific domain name, and subsequently anchored to the web host registered with that domain via DNS resolution. Effectively, this allows a DID using this scheme to be resolved as simply as one resolves a web URL, every time they click on a link. For example, we’ve set up a DID Web using our own domain, which can be resolved at

  • New to JSON-LD? Introducing JSON-LD Lint

    The rise in popularity of javascript (due to its natural language monopoly in web-browsers) led to a mass exile from XML and shift over to JSON as the prefered data representation format. In the process, certain valuable features of XML were lost, in particular those that provide a standardised semantic syntax. JSON-LD defines this missing layer of syntax, which improves semantic reasoning around data. This is critical for maintaining data quality and trust in data, which is particularly important as we increase our reliance on digital infrastructure, IOT and AI.

  • Adding support for revocation of Verifiable Credentials

    Integrating revocation into our platform brings us one step closer to building a fully realized verifiable data ecosystem, an environment where verifiers can have more confidence and trust in the decisions they’re making and people can participate in the sharing and exchange of information without eroding their basic privacy. We look forward to continuing to collaborate with the community and gathering feedback from industry to enhance and extend different ways to accomplish revocation while respecting users’ digital rights.

  • DHS Awards $200K for Issuing and Validating Essential Work and Task Licenses

    MATTR is currently building an extensive set of foundational capabilities in a software-as-a-service (SaaS) platform for verifiable credential issuance, verification, and storage. An essential worker or a person performing an essential task would receive various credentials and attestations from many issuers containing relevant assertions about their essential work or task status. Their solution also offers the option to validate the information further by using either public or private registries of authoritative verifiable information.

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