Categories: BlockChain

Decentralized Identity Management, Blockchain – Why Bother

This blog represents details on Decentralized Identity Management and why you should care? Given that IBM, Hyperledger has joined Blockchain Identity Consortium makes it much more important to quickly go over the concepts related with decentralized identity management. Check out a related Hyperledger project, Project Indy, on supporting independent identity on distributed ledgers.

Traditional Centralized or Federated Identity Management System

Conventional identity management systems have always been based on centralized authorities such as corporate directory services , certificate authorities (CA) , or domain name registries. Each of these centralized identity management systems acted as a “root of trust”.

In order to have the identity management work across different systems, there is something called as federated identity management. According to Wikipedia post of Federated Identitya federated identity in IT is the means of linking a person’s electronic identity and attributes, stored across multiple distinct identity management system.

What is Decentralized Identity Management System?

Following are some of the key points to understand decentralized identity management in better manner:

  • In decentralized identity management system, there are no centralized authorities anymore. The “root of trust” lies with Distributed Ledger. The copies of ledger are maintained by multiple participants, and thus, distributed ledger. Note that Blockchain is one of the classic example of Distributed Ledger Technology.
  • Entities no more identify themselves with traditional centralized authorities such as CAs, domain name registries etc. They rather identify themselves with the distributed ledger.
  • Each entity is assigned as Decentralized Identifiers (DIDs).
  • DIDs can be used to point what is called as DID Document.
  • DID Document may consists of one or more service endpoint that can be used to interact with the entity. This comes in a JSON format. Every DID document must have an associated DID. This is how a DID document would look like:
    {
      "@context": "https;//w3id.org/did/v1",
      "id": "did:example:123456789abcdefghi",
      "authorizationCapability": [{
        // this entity is a delegate and may update any field in this
        // DID Document using any authentication mechanism understood
        // by the ledger
        "permission": "UpdateDidDocument",
        "entity": "did:example:zxyvwtrkpn987654321"
      }],
      "credentialRepositoryService": "https://vc.example.com/abcdef",
      "authenticationCredential": [{
        // this biometric can be used to authenticate as DID ...fghi
        "id": "did:example:123456789abcdefghi/biometric/1",
        "type": "PseudonymousBiometricTemplate2017",
        "owner": "did:example:123456789abcdefghi",
        "biometricService": "https://example.com/authenticate"
        "biometricTemplateShard": "Mjk4MzQyO...5Mzg0MDI5Mwo="
      }]
    }
    
  • Following are some of the key terminologies in relation with DID:
    • DID path
    • DID fragment
    • DID normalization
    • DID persistence

For details, read the W3C Community Group Draft Report on Decentralized Identifiers. Check out a related W3C Community Group draft on verifiable claims.

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

Share
Published by
Ajitesh Kumar

Recent Posts

Agentic Reasoning Design Patterns in AI: Examples

In recent years, artificial intelligence (AI) has evolved to include more sophisticated and capable agents,…

2 months ago

LLMs for Adaptive Learning & Personalized Education

Adaptive learning helps in tailoring learning experiences to fit the unique needs of each student.…

2 months ago

Sparse Mixture of Experts (MoE) Models: Examples

With the increasing demand for more powerful machine learning (ML) systems that can handle diverse…

3 months ago

Anxiety Disorder Detection & Machine Learning Techniques

Anxiety is a common mental health condition that affects millions of people around the world.…

3 months ago

Confounder Features & Machine Learning Models: Examples

In machine learning, confounder features or variables can significantly affect the accuracy and validity of…

3 months ago

Credit Card Fraud Detection & Machine Learning

Last updated: 26 Sept, 2024 Credit card fraud detection is a major concern for credit…

3 months ago