Keywords and Concepts


A process where a user's identity is determined or confirmed. Different levels of KYC verification can unlock access to features and resources on different platforms.

Traditional KYC

Verification methods that take a user's identity documents such as passport, driving license, or other documents that are issued and verified by a centralized authority to establish their identity.

Decentralized KYC

Verification methods that establish and verify a user's identity in an ecosystem using processes that do not rely on any central authority for verification.


Decentralized identifiers (DIDs) are a new type of identifier that enables verifiable, decentralized digital identity. A DID refers to any subject (e.g., a person, organization, thing, data model, abstract entity, etc.) as determined by the controller of the DID.
An example of a DID string is did:ont:t52065e...6Av8eE09.
Refer to the W3C DID Proposed Recommendation for more details.


Verifiable credentials basically contain information that is necessary to determine if the credential is genuine. Any party can issue credentials in a trust network. That would make them a source of trust within the network. Users store and maintain credentials after they’re issued. They may choose to selectively, or totally share the data within a credential with credential consumers, who then verify it using the attested record on a public ledger such as Ontology or Ethereum.
Refer to the W3C Verifiable Credentials docs for more details.

Model Provider

A role within the Orange protocol. Abbreviated as MP. MPs put together models that they may choose to use for themselves, or make available for public use.


Models define the way data is used to determine the reputation for a DID. You can create and customize models to define the operations that are performed on the data and the weightage that is assigned to data. Each model takes a set of useful data and generates a report depending on the design.

Data Provider

A role within the Orange protocol. Abbreviated as DP. DPs put together datasets that they may choose to use for themselves or make available for public use.


Datasets are collections of on-chain or off-chain data that can be plugged into models. They can be classified based on the platform or ecosystem they are fetched from, and the nature of the use case they were collected in. To plug a dataset into a model, its schema must match with the input parameters of the said model.
Useful data from different sources can also be compiled together by DPs to form a dataset.