The Anatomy of a Trusted Data Market

trusted data markets cheqd blog

A deep dive into the fundamentals of what a Trusted Data Market is and how cheqd’s infrastructure enables them.

This is part of a series, read the second blog ‘The role of cheqd in Trusted Data markets’ here.

In short, a “Trusted Data Market” is a vision for both consumers and businesses where the paradigm of data ownership is inverted to the user, trust is verifiable, and can be transacted upon within a privacy-preserving data market.¹


If you look up the word “trust” in the dictionary, the first definition is typically one about general trust between two parties, but almost always, the second definition is a financial one. Take, for example, the language around finance; the United States is backed “by the full faith and credit” of the U.S. government. “Credit,” from the Latin credere, literally means “to trust.” Trust is known to be fundamental to better economic outcomes² and we all trust in others and reciprocate that trust with trustworthy actions as part of our everyday lives. Moreover, economic historians debate the relative importance of models of trade growth and the importance of capital infusion to fuel innovation. But where there is broad agreement is in the central notion that goes back to civilisation’s origin: trust undergirds cooperative behaviour. But what is trust’s central role in markets? And how could verifiable data change trust’s role in markets to form a new data paradigm?

Defining Trust

Philosophers have an important definition of what ‘trust” is. Most agree the dominant paradigm of trust is “interpersonal” and this type of trust is a type of reliance, although it is not mere reliance.³ Rather, trust involves reliance “plus some extra factor.”⁴ Although this definition is lacking when applied to trust in current data markets, we will explore later its applicability within a trusted data market.

This extra factor typically concerns why the trustor (i.e., the one trusting) ought to rely on the trustee to be willing to do what they are trusted to do. This is further conditioned by another layer of trust: whether the trustor is optimistic that the trustee will have a “good” motive for acting. This is especially important should the trusted interaction involve a transactional relationship (the exchange of assets or risk), to optimise incentive alignment within a market context.

This is demonstrated in the logic of why one should trust someone.

T(x,y) = x trusts y

R(x,y) = x relies on y

E(x,y) = x ought to believe there is an ‘extra factor’ for trusting y

W(x,y) = y is willing to do what they are trusted to do

M(x,y) = y acts with good motive

For all x and y,

T(x,y) → [R(x,y) ∧ E(x,y) ∧ W(y) ∧ (T(x,y) → M(y))]

This translates to: for all x and y, x trusts y iff (if and only if) x relies on y and believes there is an extra factor for trusting y and y is willing to do what they are trusted to do (not dependent on x trusting y) and if x trusts y, then x believes that y has a good motive for acting. This logic breaks down if y would not be willing to do what they were originally trusted to do and/or y acts with a “bad” motive.

Critically, to establish these relations as “trusted,” time and repetition are necessary. The more “trusted interactions” are performed successfully, the more likely one can safely assume trust as I can likely assign a ‘good’ motive for acting, and admit a history of actions than consistently show y is willing to do what they are trusted to do.

In this blog, we’ll explore how a market dynamic could shape if certain trust conditions in our introduction became verifiable from inception, what this subsequently could mean for time-to-reliance to form trust, and the consequences in terms of transparency, accountability, and reliability this could entail. We will then explore how this could form a “Trusted Data Market.”

The Role of Trust in Markets

Trust is, and always has been, an essential component of markets. Therefore, we must ascertain why trust formed in markets, and why sociologists and anthropologists cite the advent of market economies as representing a significant break in the organisation of human societies.

From an economist’s perspective, markets represent a transition from a system where product distribution was based on personal relationships to one where distribution is governed by transparent rules. While these rules are necessary to ensure that markets operate effectively, trust is also crucial to ensure that transactions are conducted in good faith, and to promote compliance with market regulations. The rules themselves now represent a ‘shared truth’ which replaced the previous social contracts and interpersonal relationships that pre-dated market economies.⁵

Primarily, trust reduces the risk of opportunistic behaviour within rule-based frameworks. When buyers and sellers trust each other to act in good faith, they are more likely to engage in mutually beneficial transactions. As described above, these ‘trusted interactions’ form a basis for trust over time. Therefore, trust is an important property for promoting compliance with market regulations to ensure that the market functions efficiently and that all participants can compete fairly. Concerning data, trust is also critical for promoting transparency and accountability in markets with rules. When buyers and sellers trust each other, to be honest, and transparent in their transactions, they are more likely to provide accurate information about the goods and services they offer. This promotes transparency in the market and helps to ensure that all parties have access to the information they need to make informed decisions. Moreover, trust can also foster self-regulation and promote agency, defined by Hickman et al. as:

“… intentionality, responsible for defining strategies and plans; anticipation, related to temporality, in which the future tense represents a motivational guide, driving force of prospective acts to reach goals; self-regulation, which are personal patterns of behaviours that monitor and regulate their actions; self-reflection, responsible for self-inquiry into the value and meaning of their actions.”⁶

And with agency undergirded by trust, businesses and individuals who are trusted by their peers are more likely to adhere to ethical standards and social norms.

For these advancements to occur, verifiable data could reduce the need for trust (its current role in markets), as defined by time + trusted interactions, by ensuring data integrity and authenticity, promoting fair competition, reducing fraud risk, improving transparency, and simplifying regulatory enforcement. Forming a powerful tool for modern rule-based markets’ efficiency and effectiveness and ability to innovate with new use cases, commercial models and trust dynamics.

Data in Markets

Data is well-referenced as the “new oil” of the digital economy. This is perhaps more evident than ever before as its driving innovations like Chat GPT, creating new knowledge and insights from various machine learning and reasoning techniques, and increasing efficiency in many fields.⁷

However, certain types of data markets are not transparent where the user is the product. Typically, consumers are not cognisant of what happens to their data, and without self-sovereign identity, cannot control data use.⁸ Researchers and academics have persuasively argued a legitimate trade of data in a “shadow market”⁹ has evolved, however, this “shadow market” (intermediary platforms that trade on subject’s data) is not lucrative for the subjects of data, but is for data controllers. This has presented a conflict and misalignment of incentives between consumers’ data rights, assumed privileges and increasing desire for privacy and the current market demands for data.

A vision for both consumers and businesses, where the paradigm of data ownership is inverted to the user, and trust is verifiable within a privacy-preserving data market¹⁰ is what we at cheqd refer to as a “Trusted Data Market” powered by cheqd’s infrastructure.

cheqd’s infrastructure provides privacy protection and verifiable informational self-determination for consumers. Inverting the current Data Market paradigm whilst critically providing economic advantage and innovation for businesses who currently control and participate in the preexistent data economy. This type of data sharing, when willingly shared by “Issuers’ (companies who own, control and monetise consumer data) can provide verifiable information, through cheqd’s verifiable data registry and infrastructure, shared by ‘Holders’ (consumers, customers, companies or even objects within a supply chain) can afford low market entry barriers, transparency for the parties involved as well as verifiable data. And critically, new economic data models where trust has a new property within the data paradigm: verifiability, forming the foundation for a new, innovative type of Data Market.

The Trust Game

Before bringing this all together, it’s important to note what Verifiable Data can solve within economic theory for Data Market models. Economic theory commonly models this through a thought experiment known as the “Trust Game.” The “Trust Game” involves a principal-agent scenario where the following economic features of trust — risk, shared values, sacrifice, and reputation — are observed. At its core, the game involves a sequential exchange in which there is no contract to enforce agreements. In the game, most variants of it endow subjects with $X. Subjects are then paired anonymously and assigned to either the role of “sender” or “receiver.” Stage 1: The sender (trustor) may either pass nothing or any portion X of the total Y to the receiver (Trustee). If the sender keeps X, the experiment conditions triple the total amount so that 3x is passed onto the receiver. Stage 2: The receiver (trustee) may either pass nothing or pass any portion Y of the money received back to the sender. The amount passed by the sender is said to “capture trust,” as it infers an intent that the other party “… will reciprocate a risky move (at a cost to themselves),’’ and the amount returned to the trustor by the trustee, therefore, capture trustworthiness.¹¹

Importantly, the “Trust Game” models how trust occurs in the context of a cooperative relationship with repeated interactions over time. In iterations of market dynamics where trust must be assumed, gained, reciprocated, and then maintained, we can only gage trust by how parties act within a transactional instance, and how the accumulation of these trusted interactions forms “trust” and creates dependencies on how those actions will affect cooperation in the future. Simultaneously, how market participants behave today is also determined by cooperative behaviour in the past.

In the Trust Game model, repeated interactions over time are the critical factor in determining whether those interactions can be fairly assumed as worthy of a trusted reputation. Players may initially be cautious and invest a small amount, and then gradually increase their investment as they learn more about the other player’s trustworthiness. This may not always lead to high levels of trust and cooperation between the players.

However, if verifiable trust is established from inception, players would have access to data about each other’s trustworthiness before the game begins. For example, they may have access to ratings or reviews from previous games, or other forms of verifiable data that indicate the other player’s trustworthiness, like data which is issued from a trusted issuer that provides assurance the information the player presents has an “extra factor” other plays can rely upon. This can create a positive expectation and lead to more initial trust between the players, reducing the need for a learning process to establish trust. Time-to-reliance within the market dynamic shifts, regardless of the use case as the verifying participant can assume the data is at base verifiable.

For example, in the Trust Game’s model, if the players have access to verifiable data indicating that the other player has a history of trustworthy behaviour in previous games, they may be more likely to invest a larger amount of money in the current game. This can lead to a higher degree of cooperation and reciprocity between the players, resulting in higher payoffs for both players and they may be more likely to view them in a positive light and exhibit more cooperative behaviour.

This also reduces the complexity of why a rational agent should trust someone. To travel back to our previous logic, let’s see how it becomes modified:

For all x and y,

T(x, y) if and only if [(R(x, y) ∧ P(x, y))]


P(x, y) = x has positive reasons (verifiable trust) to assume or validate that y is trustworthy.

trusted data markets cheqd blog
But how can we alter the time variable, and provide a verifiable “extra factor” to form a model where participants can establish that trust is warranted from inception in market dynamics?

Trusted Data Markets

In two essential references for this blog, Altman defines privacy as “the selective control of access to the self,”¹² and Mason describes the individual who trades private information about the self as a kind of currency in exchange for anticipated goods and services.¹³ Essentially, I should be able to exercise personal agency, select, control and subsequently act (for example, trade) upon information that accesses my personal data and determine with whom I share that access with. This is not a new digital precedent but has been deliberated on in terms of dignity, exceptionalism, and values by philosophers for centuries.¹⁴

Many other well-worked cheqd blogs on self-sovereign identity, trusted data, and cheqd’s payment infrastructure explain how self-sovereign identity and cheqd’s infrastructure can facilitate this paradigm, both from a technical and commercial perspective. What we’ll dive into in this blog is the relevance of this type of data, and selective control of it, to a market dynamic.

In a cheqd trusted data market, “holders” (users, companies, objects) have selective control, and their willingness to share data is dependent on a variety of factors, e.g: benefits, type of information, programming and culture.¹⁵

In “Trusted Data Markets,” companies issue verifiable data to holders, who in turn actively share their data with interested parties (known as “Verifiers”) who wish to verify that data. The reasoning behind this dynamic forming is multifold, but we will focus on commercial benefit for “Issuers”, and we will explore various use cases in subsequent blogs where Trusted Data Market dynamics form around a payment flow: “Verifier pays Issuer.”

Within a “shadow” data market, this payment has already formed, without the user, as we all already interact within Data Markets, but our data is traded upon without our selective control. With cheqd and self-sovereign identity, this paradigm is inverted via a privacy-preserving, standards-compliant data and payment infrastructure. This infrastructure forms the structure for both verifiable trust and payments to support the transactional flows of associated verified and trusted data within the format of verifiable credentials.

The “Issuer” issues Verifiable Data

The “Holder” receives this data, which can be trusted as 100% verifiably issued by the Issuer.

The “Holder” then presents said data (a Verifiable Credential) to the “Verifier/Receiver.

Upon presentation, in which the “Holder” maintains selective control, the Verifier can “check” the verifiability of the Verifiable Credential (the data), via cheqd’s network, and upon this “check” ascertain whether the data is: verifiability issued by the issuer, non-revoked, and of the correct standards.

It is via this “check” a privacy-preserving payment is released from the “Verifier/Receiver” to the “Issuer.” At no point within this market dynamic is selective control of the data removed from the “Holder” and at no point is the presentation of the “Holder’s” data gated by a payment wall.

The verifier can ascertain greater trust in the credential received, via the reputation of the issuer reducing time-to-trust and trust the data issued is from the issuer at genesis.

The price a Verifier is willing to pay correlates to the impact of Verifiable Trust on the market dynamic.

This price is set by the Issuer.

Crucially this solves two significant problems for data markets.


Typically, trust takes significant time to develop and maintain and this in turn informs market dynamic structures. With the import of Verifiable Data, time-to-reliance in the market dynamic is significantly improved. If I can ascertain as a “Verifier” the data Issued to the Holder is 100% from the Issuing participant, I can mitigate the risk of fraud and form a new trust dynamic. This, in turn, can form new reputation metrics, and new commercial models spurring growth as this data has an associated value attached to it for all market participants.


Selective control of data, in a privacy-preserving fashion for “Holders,” where they can meaningfully participate in a new data market paradigm is established. No longer will users participate in “shadow” data markets, they can meaningfully participate and retain ownership and control of their information. Whether this is to access benefits, or indeed indicate what they “prefer,” this data will form a value category which we believe will create better economic outcomes.


cheqd’s trusted data markets present novel solutions for both problems, institutional and consumer participants, whilst maintaining privacy-preserving transactions and interactions, a new data paradigm emerges; with verifiable trust. One where the user is at the centre of their own data universe, and institutions can discover new revenue streams, new reliable ‘trusted’ data, new ways to innovate with customer data and participate in an emerging paradigm fit for the new data economy.

Learn more

We will be following this initial blog with a deep dive into how cheqd’s infrastructure supports the advent of Trusted Data Markets, followed by specific use cases we’re exploring. Beginning with credit data.

If you’d like to learn more, please reach out to us directly: [email protected]

[1] Gkatzelis, V., Aperjis, C., & Huberman, B. A. (2015). Pricing private data. Electronic Markets, 25(2), 109–123. s12525–015–0188–8

[2] Arrow, K. (1972). Gifts and exchanges. Philosophy and Public Affairs, I, 343–362, Fukuyama, F. (1995). Trust. New York: Free Press, Putnam, R. (1993). Making democracy work: Civic traditions in modern Italy. Princeton, NJ: Princeton University Press.

[3] Goldberg, Sanford C., (2020), “Trust and Reliance”, in Simon 2020: 97–108.

[4] Hawley, Katherine, (2014(, “Trust, Distrust and Commitment”, Noûs, 48(1): 1–20. doi:10.1111/nous.12000


[6] pp. 30–32

[7] Spiekermann, S., Acquisti, A., Böhme, R., & Hui, K. L. (2015). The challenges of personal data markets and privacy. Electronic Markets, 25(2), 161–167. 0191–0

[8] Spiekermann, S., & Novotny, A. (2015). A vision for global privacy bridges: Technical and legal measures for international data markets. Computer Law and Security Review, 31(2), 181–200. 10.1016/j.clsr.2015.01.009.

[9] Conger, S., Pratt, J. H., & Loch, K. D. (2013). Personal information privacy and emerging technologies. Information Systems Journal, 23(5), 401–417.

[10] Gkatzelis, V., Aperjis, C., & Huberman, B. A. (2015). Pricing private data. Electronic Markets, 25(2), 109–123. s12525–015–0188–8

[11] Camerer, C. (2003). Behavioral game theory: Experiments in strategic interaction. Princeton, NJ: University Press, Princeton.

[12] Altman, I. (1976). Privacy — a conceptual analysis. Environment and Behavior, 8(1), 7–29.

[13] Mason, R.O., Mason, F., Conger, S. & Pratt, J.H. (2005). The connected home: poison or paradise. Proceedings of Academy of Management Annual Meeting, Honolulu, HI, August 5–10

[14] Floridi, Luciano, On Human Dignity and a Foundation for the Right to Privacy (April 26, 2016). Available at SSRN: or

[15] Hallam, C., & Zanella, G. (2017). Online self-disclosure: The privacy paradox explained as a temporally discounted balance between concerns and rewards. Computers in Human Behavior, 68, 217–227.

cheqd is now supported in’s SSI Kit!

Integration of cheqd into SSI Kit provides greater flexibility for adopters of cheqd, opens up a new customer-base for increased utility on the network and helps future-proof cheqd for upcoming EU regulations!


We are excited to announce that cheqd is now fully supported in’s SSI Kit. This integration expands the support for cheqd in a greater array of SDKs, and provides end-customers the flexibility to choose a wider breadth of options for credential exchange protocols.

SSI Kit leverages the cheqd/sdk, slotting neatly alongside other supported SDKs including Veramo, and the soon to be released Aries SDKs, offering a wide range of SDK choices for SSI app developers which they can select dependent on their needs and existing stack.

What is SSI Kit?

SSI Kit is a holistic and standard-compliant open source tool created and maintained by the team at It offers everything you need to use Self-Sovereign Identity (SSI) with ease, including the creation, issuance, management and verification of Verifiable Credentials across various ecosystems.

SSI Kit utility for different parties docs — SSI Kit | Basics — Learn what the SSI Kit is.

Integrating cheqd with SSI Kit provides an array of benefits for both cheqd and’s existing end-customers and users:

cheqd customers:

Supporting cheqd within the SSI Kit means that anyone that wants to use cheqd, can now do so through’s intuitive and easy to use tools — available here. Through this, SSI developers can:

  • Create DID — Create your first did:cheqd
  • Issue VC — Issue your first Verifiable Credential based on a did:cheqd
  • Verify VC — Verify your Credential based on a did:cheqd

This offers cheqd’s customers:

  • Greater flexibility for end-customers: Through expanding support for cheqd into SSI Kit, end-customers can now choose a more specific technical stack that suits their needs best — with Veramo and Aries Framework JavaScript as other enterprise options.
  • Simple APIs for credential operations: offers a selection of enterprise-ready APIs for creating, updating and revoking credentials. With this new integration, all of the operations can be carried out with cheqd DIDs which makes integrating cheqd DIDs, DID-Linked Resources and Credentials into client applications lightweight and simple!
  • Future proofed for upcoming regulations: SSI Kit uses the OpenID for Verifiable Credentials stack for establishing peer-to-peer connections and for credential exchange. This is notable because it aligns with the proposed European Digital Identity Architecture and Reference Framework, which will accompany upcoming European regulations such as eIDAS v2.
  • Streamlining the bridge from Web2 identity into Web3: Through’s IDP kit, (Identity Provider Kit) cheqd customers can use cheqd issued Verifiable Credentials with traditional identity infrastructure, such as IAM tools including KeyCloakgluu and Okta. customers:

The SSI Kit abstracts complexity for developers by following a “multi-stack approach” that enables developers to use different implementations or “flavours” of SSI. Adding cheqd as the latest “flavour” offers walt’s customers all the benefits cheqd has to offer, including:
  • Support for DID-Linked Resources: cheqd is the first identity network to build and support DID-Linked Resources (now a draft W3C standard) to support various identity data structures such as schemas, trust registries and status lists. Support for cheqd enables’s existing customer-base to utilise this innovative functionality.
  • Support for upcoming Payment Rails: cheqd’s vision is to become the de-facto payment mechanism for trusted data. By supporting cheqd within the SSI Kit,’s customers can benefit from already having existing integrations with cheqd, making it far easier and faster to leverage payment rails when released.
  • Offering a higher performance network at a lower cost: cheqd is designed as a highly performant Layer 1 with high throughput. cheqd can process an estimated 7,500 Transactions Per Second (TPS), benchmarking well beyond other leading networks such as Cardono (250 TPS), Ethereum (15–30 TPS), Avalanche (5000 TPS) and Bitcoin (10 TPS). Gas fees on cheqd are a fraction of the cost of other networks, making it far cheaper to transact on the network.

Why is’s SSI Kit important for cheqd?

When it comes to Self-Sovereign Identity, there are different technical components that need to work together to construct an end-to-end solution. The combination of different protocols together to make-up a tech stack is vital for interoperability between different ecosystems.

The Trust over IP Foundation describe these different components clearly within the ToIP Stack:

Trust over IP (ToIP) Stack

With reference to the image above, cheqd sits at the Layer 1 of the stack; cheqd is a Verifiable Data Registry with a DID method which supports the anchoring of DIDs and associated DID-Linked Resources.

SSI Kit works at Layers 2 and 3 of the stack, supporting a suite of protocols for credential exchange and peer-to-peer connections which hit a different market compared to those cheqd supports in its other SDKs. These include: OAuthOpenID for Verifiable Credential Issuance (OpenID4VCI)OpenID for Verifiable Presentations (OpenID4VP) and Self-Issued OpenID Provider v2 (SIOP V2).

The image below offers a cheqd specific overview which helps to further illustrate SSI Kit’s place in the stack.

cheqd capability model

By supporting these protocols, it gives end-customers more flexibility in choosing a tech stack that fits on top of their use case, jurisdiction and existing identity management systems. This is especially important as:

  1. The OpenID for Verifiable Credential stack is closely related to OpenID Connect in terms of how the authentication flows work between different parties. This makes it less daunting for companies to transition from something more traditional or federated, such as OpenID Connect, to decentralised identity.
  2. The OpenID for Verifiable Credential protocols are also supported by a range of prominent SSI vendors, such as Microsoft (Entra), MattrYesPing and Workday within the VC-JWT Presentation Profile, meaning that cheqd can now support and interoperate with a wider array of large vendors and their clients.
  3. These protocols form part of the European Digital Identity Architecture and Reference Framework, which is a new interoperability profile for companies to exchange trusted data in the European Union. Conforming with the technical stack described here will help future-proof cheqd’s tech stack for the upcoming European regulatory changes, which will give legal effect for credentials as a means of data exchange.

If you are interested in learning more about these regulatory changes, we would recommend that you read Avast’s takeaways from the regulatory changes, or watch Nacho Alamillo’s presentation on the proposed eIDAS 2 Regulation.

A bright future ahead

Interoperability, flexibility, simplicity and cost-efficiency are the key ingredients for adoption of Self-Sovereign Identity. With an eye on all of these, cheqd is positioning itself strategically for any vendor or organisation looking to implement an SSI solution. SSI Kit was the perfect storm for providing another enterprise software product, while also covering a new set of connection credential and exchange protocols.

Oh, and if you made it this far — we also have a lot of exciting developments to come, using this tech stack and the cheqd <> partnership 🔜👢👢

As always, if this blog resonates with you and you want to learn more about building on cheqd, please get in touch with our product team here and cheq-out our identity documentation here.

Universal Registrar: DID utility off-the-shelf


cheqd’s new Universal Registrar driver enables easy and efficient integration with cheqd’s DID and DID-Linked Resource utility.


We are excited to announce that we have successfully built a cheqd driver into Decentralized Identity Foundation’s (DIF) Universal Registrar to enable out-of-the-box and highly efficient DID and DID-Linked Resource transactions on cheqd. This is a big step in simplifying the developer journey for client applications to use cheqd’s DID and DID-Linked Resource utility in a more light way than integrating with our Software Development Kits (SDKs).

Understanding the value of the Registrar

The Universal Registrar is an open source application created by the Decentralized Identity Foundation (DIF) which aims to make it far easier to create, update and deactivate Decentralized Identifiers (DIDs) across a range of DID Methods without full integration.


The aim of the Universal Registrar is similar to the Universal Resolver; to transform method-specific APIs for DID transactions into a common format for client applications to easily call. In more simple terms, remember the kids’ toys with different shapes and different shaped holes? Yep this one!

Imagine each different DID Method driver is a different shape. If you run an application and have to consume all different shapes and sizes, that is a hugeeeee uplift to maintain. What the Universal Registrar does is converts all of these shapes into one common shape which makes it far easier for any application to consume any of the listed DIDs (in technical terms it wraps an API around a number of co-located Docker containers).

DID Operations with minimal integration

Not only does it make it easier for client applications to support DIDs from multiple DID methods, but it also makes it far quicker and easier to create, update and deactivate DIDs — as it calls the method-specific driver with a common API.

If you imagine our SDK as a flatpack #IKEA product for DIDs, where it’s simple to put together, but you have to have instructions & the right tools (and a bit of patience).

Then imagine the Universal Registrar is like buying cheqd DID functionality straight off-the-shelf — it’s simple, efficient and quick! And it allows our partners or customers to use cheqd’s utility within minutes.

Therefore, the barrier for integrating cheqd DIDs into existing client applications has been greatly reduced by the Registrar. Instead of having to integrate with the cheqd SDK, applications can now create a simple workflow to call the relevant APIs for issuing, updating or deactivating cheqd DIDs and creating DID-Linked Resources.

Going beyond other DID Registrar Drivers

cheqd’s DID Registrar driver also supports the creation of DID-Linked Resources which goes beyond any other existing DID Method on the market. This provides the functionality for any developer to easily create the likes of schemas, trust registries and status lists on cheqd.

This week, the W3C has also formally approved the DID-Linked Resource work item which will be developed as a formal standard over the next few months here! 🥳

Getting started with the Registrar

We have created a simple setup guide for using the Registrar with Docker or locally. You can also find us on the Universal Registrar frontend.

Once you have setup the registrar, you can use the cheqd Registrar driver APIs here coupled with the Universal Registrar to build into your workflows!

For more information, we have created an Architecture Decision Record which describes the workflow for building cheqd DIDs and DID-Linked Resources into existing client applications using the Registrar.


We were clear in our Product Vision blog for 2023 that the path to adoption for cheqd goes hand in hand with the simplicity of integrating with its identity functionality. Using a DID Registrar abstracts away a lot of the complexity of fully integrating with cheqd’s SDK, but provides all the same benefits for DIDs and DID-Linked Resources. This is therefore a huge step in gaining wider adoption in a broad array of applications and SDKs, as the uplift for supporting cheqd DIDs is now much simpler. As always, if this blog resonates with you and you want to learn more about building on cheqd, please get in touch with our partnerships team here or you can try out our SDK for issuing and verifying credentials here, and you can setup the DID Registrar here! We set out at the beginning of 2022 to integrate cheqd into the DIF Universal Resolver. The Universal Resolver utilises REST APIs and other interfaces to enable the resolution of any DIDs which have a supported driver. We have successfully made this integration and you can now find did:cheqd on the list of supported drivers. Over 2023, we will improve and refactor our DID Resolver and our integration to make it fully enterprise-ready. The graph below shows our work on the cheqd DID Resolver and how the bulk of the work was carried out towards within the second and third quarter.