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Tokenizing Virtual Identity: Blockchain & AI’s Inevitable Impact

Tokenizing Virtual Identity

Tokenizing virtual identity is the latest buzzword in the world of technology. With the rise of blockchain and AI, the process of tokenizing virtual identity has become more feasible and efficient. In a world that is increasingly dependent on digital communication and transactions, virtual identity has become an essential aspect of our lives. From social media to online banking, virtual identity is crucial for individuals and organizations alike. This article explores the inevitable impact of blockchain and AI on tokenizing virtual identity.

What is Blockchain and AI?

To understand the role of blockchain and AI in tokenizing virtual identity, we need to first understand what these technologies are. Blockchain is a decentralized and distributed digital ledger that records transactions across multiple computers, allowing secure and transparent storage of data. AI, on the other hand, refers to the simulation of human intelligence in machines that can perform tasks that typically require human cognition, such as learning, reasoning, and problem-solving.

The Benefits of Tokenizing Virtual Identity

Tokenizing virtual identity offers several benefits. Firstly, it provides a higher degree of security than traditional identity management systems, as it is based on cryptography and decentralized storage. Secondly, it offers greater control and ownership of personal data, allowing individuals to manage and monetize their identity. Thirdly, it offers greater efficiency by reducing the need for intermediaries and streamlining identity verification processes.

The Role of Blockchain in Tokenizing Identity

Blockchain plays a crucial role in tokenizing virtual identity. By providing a decentralized and secure platform for storing and managing identity data, blockchain ensures that personal data is owned and controlled by individuals, rather than centralized institutions. Blockchain also enables the creation of self-sovereign identities, where individuals have complete control over their identity data and can share it securely with trusted parties.

The Role of AI in Tokenizing Identity

AI plays a crucial role in tokenizing virtual identity by automating identity verification processes. By leveraging machine learning algorithms, AI can analyze large volumes of data and make intelligent decisions about identity verification. This can help reduce the risk of fraud and improve the efficiency of identity verification processes.

Tokenizing Virtual Identity: Use Cases

Tokenizing virtual identity has several use cases. For example, it can be used for secure and decentralized voting systems, where individuals can verify their identity and cast their vote securely and anonymously. It can also be used for secure and decentralized identity verification for financial and healthcare services, reducing the risk of identity theft and fraud.

Tokenizing Virtual Identity: Challenges

Tokenizing virtual identity also presents several challenges. One of the main challenges is interoperability, as different blockchain networks and AI systems may not be compatible with each other. Another challenge is scalability, as blockchain and AI systems may not be able to handle the volume of data required for identity verification on a large scale.

Security Concerns in Tokenizing Identity

Security is a key concern in tokenizing virtual identity. While blockchain and AI offer greater security than traditional identity management systems, they are not immune to attacks. Hackers could potentially exploit vulnerabilities in blockchain and AI systems to gain access to personal data. It is therefore crucial to implement robust security measures to protect personal data.

Privacy Issues in Tokenizing Identity

Privacy is another key concern in tokenizing virtual identity. While tokenizing virtual identity offers greater control and ownership of personal data, it also raises concerns about data privacy. It is essential to ensure that personal data is not shared without consent and that individuals have the right to access, modify, and delete their data.

Legal Implications of Tokenizing Identity

Tokenizing virtual identity also has legal implications. As personal data becomes more valuable, it is crucial to ensure that there are adequate laws and regulations in place to protect personal data. It is also essential to ensure that individuals have the right to access and control their data, and that they are not discriminated against based on their identity.

The Future of Tokenizing Virtual Identity

The future of tokenizing virtual identity looks bright. As blockchain and AI continue to evolve, we can expect to see more secure, efficient, and decentralized identity management systems. We can also expect to see more use cases for tokenizing virtual identity, from secure and anonymous voting systems to decentralized identity verification for financial and healthcare services.

Embracing Blockchain & AI for Identity Management

In conclusion, tokenizing virtual identity is an inevitable trend that will revolutionize the way we manage identity. By leveraging blockchain and AI, we can create more secure, efficient, and decentralized identity management systems that give individuals greater control and ownership of their personal data. While there are challenges and concerns associated with tokenizing virtual identity, these can be addressed through robust security measures, privacy protections, and adequate laws and regulations. As we continue to embrace blockchain and AI for identity management, we can look forward to a more secure, efficient, and decentralized future.

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Responsible AI – Privacy and Security Requirements

Training data and prediction requests can both contain sensitive information about people / business which has to be protected. How do you safeguard the privacy of the individuals? What steps are taken to ensure that individuals have control of their data? There are regulations in countries to ensure privacy and security.

 In Europe you have the GDPR (General Data Protection Regulations) and in California there is CCPA (California Consumer Privacy Act,). Fundamentally, both give an individual control over its Data and requires that companies should protect the Data being used in the model. When Data processing is based on consent, then am individual has the right to revoke the consent at any time.

 Defending ML Models against attacks – Ensuring privacy of consumer data:

 I have discussed about very briefly about the tools for adversarial training – CleverHans and FoolBox Python libraries here: Model Debugging: Sensitivity Analysis, Adversarial Training, Residual Analysis  . Let us now look at more stringent means of protecting a ML model against attacks. It is important to protect the ML model against attacks, thus, ensuring the privacy and security of data. An ML model may be attacked in different ways – some literature classifies the attacks into: “Information Harms” and “Behavioural Harms”. Information Harm occurs when the information is allowed to leak from the model. There are different forms of Information Harms: Membership Inference, Model Inversion and Model Extraction. In Membership Inference, the attacker can determine if some information is part of the training data or not. In Model Inversion, the attacker can extract all the training data from the model and Model Extraction, the attacker is able to extract the entire model!

 Behavioural Harm occurs when the attacker can change the behaviour of the ML model itself – example: by inserting malicious data. In this post – I have given an example of an autonomous vehicle in this article: Model Debugging: Sensitivity Analysis, Adversarial Training, Residual Analysis

Cryptography | Differential privacy to protect data

You should consider privacy enhancing technologies like Secure Multi Party Computation ,(SMPC) and Fully Homomorphic Encryption (FHE). SMPC involves multiple systems to train or serve the model whilst the actual data is kept secure

In FHE the data is encrypted. Prediction requests involve encrypted data and training of the model is also carried out on encrypted data. This results in heavy computational cost because the data is never decrypted except by the user. Users will send encrypted prediction requests and will receive back an encrypted result. The goal is that using cryptography you can protect the consumers data.

Differential Privacy in Machine Learning

Differential privacy involves protection of the data by adding noise to the data so that the attackers cannot identify the real content. SmartNoise is an open-source project that contains components for building machine learning solutions with differential privacy. SmartNoise is made of following top level components:

✔️Smart Noise Core Library

✔️Smart Noise SDK Library

This is a good read to understand about Differential Privacy:

 Private Aggregation of Teacher Ensembles (PATE)

This follows the Knowledge Distillation concept that I discussed here: Post 1- Knowledge DistillationPost - 2 Knowldge Distillation. PATE begins by dividing the data into “k” partitions with no overlaps. It then trains k models on that data and then aggregates the results on an aggregate teacher model. During the aggregation for the aggregate teacher, you will add noise to the data and the output.

For deployment, you will use the student model. To train the student model you take unlabelled public data and feed it to the teacher model and the result is labelled data with which the student model is trained. For deployment, you use only the student model.

The process is illustrated in the figure below:

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PATE (Private Aggregation of Teacher Ensembles)



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What Is Distributed Identity? How Decentralized ID Works

Distributed digital identity, decentralized identity, blockchain, and distributed ledgers: what do they mean and how can they help keep my company secure?

What is a digital identity? A digital identity is information that combines all your personal online activities and data. Examples of what would make up your digital identity include usernames, passwords, online searches, date of birth, and social security number.

What Is the History of Digital Identity?

Digital identity is a critical and ever-present part of our lives. Identities play a role in almost every aspect of our lives, from business to commerce to entertainment. Additionally, many jurisdictions are turning to digital identity as civic documentation to cover identification purposes outside of the private sphere.

The history of digital identity has followed security, privacy, and usability questions, with different technologies attempting to address various aspects of these categories. One of the central challenges to digital identity has been centralization.

Centralization brings a host of problems to administrators, enterprises, and users alike:

  • Central Points of Failure: Centralized identity relies on central control over the implementation of that identity, which often means on-premise databases of login credentials (typically usernames and passwords or PINs). If that database is hacked, then those credentials are compromised and all user information has most likely been exposed.
  • Usability and Security Practices: Centralized identity schemes force organizations to either adopt outside identity management systems or implement their own—a reality that has led to a fragmentation of identity management. Users have to remember individual credentials for multiple systems, leading to poor security (from simple or reused passwords) and identity theft.
  • Lack of Ownership: The question of digital identity ownership is a lively one, with different regulations and business practices vying for control of private information. Centralized identity management requires that organizations mediate control between digital identities and users rather than placing ownership in the users’ hands.

Modern identity and access management have worked toward addressing some of these issues, primarily to support a connected, cloud-based, and secure digital world.

One of the emerging technologies to address these issues is single sign-on. The goal of SSO (also known as federated identity) is to facilitate authentication across multiple systems using a centralized repository of identities and policies.

Generally speaking, there are a few protocols through which SSO works:

Security Assertion Markup Language

SAML is an open markup language used by identity providers to format and transmit authorization credentials to other platforms or service providers. The idea is that a centralized SSO provider manages identities through a server and formats SAML authentication through an XML-based token system that connects identity providers and service providers (the organization handling your identities and the company with which you want to authenticate).

Open Authorization

As the name suggests, OAuth is more an authorization approach than an authentication method, but it can be used as part of an SSO scheme. Unlike SAML, where federation happens from a centralized identity provider across multiple service providers, it’s more often the case with OAuth that a user in an authorized session with one provider can access another provider from that session.

Of course, it bears stating that SSO is a smaller part of the larger discipline of IAM explicitly focused on how to provide federated identity and authentication without compromising security.

The problem with SSO and IAM, in general, is that they only address a small subset of issues with centralized SSO or OAuth. To start with, SSO systems still have security issues, and a compromised identity provider will still pose a risk to all users. Additionally, none of this addresses the issue of identity and data ownership.

To take steps in facing some of these lingering issues, developers and scientists are working toward developing distributed identities.

What Is Distributed Identity?

Distributed identity, also called decentralized identity, is the practice of truly removing the centralized nature of identity management from the equation.

Instead of creating localized or platform-specific usernames that rely on a single organization or consortium of participating organizations to manage, decentralization uses technology to place ownership of identity data into the hands of the users that information is supposed to represent.

How is this possible? The truth is that there isn’t a clear-cut answer yet but rather a collection of technologies that are stepping up to introduce decentralization into IAM as a whole:

  • Blockchain: Originally introduced in cryptocurrencies, like Bitcoin, as part of the nascent “Web 3.0,” the blockchain has been isolated as a uniquely powerful technology that provides an immutable, decentralized ledger of ownership. Under a blockchain, users have programs called wallets that store information and denote ownership, and this ownership is not dependent on a central organization to manage.
  • Decentralized Identifiers: Created by the World Wide Web Consortium, DID is a scheme of identity decentralization outside of blockchains proposed as a general protocol for managing identity. With DIDs, users can control their data, be protected by cryptography, and authenticate with participating organizations.

The blockchain, in particular, is part of what is currently being dubbed Web 3.0, emphasizing decentralization of control over information. It works by creating a ledger that the users of that network control through their participation, protected with cryptography.

Why Is Distributed Digital Identity So Important?

Right now, data ownership and protection are critical questions for large enterprises, governments, and end users alike. The General Data Protection Regulation is one of the most stringent privacy and security jurisdictions globally, due in no small part to its driving mission to place control of private data into the hands of consumers.

But giving users control over their digital identity and their personal data is no small task. Data is often seen as ephemeral, and users in many places (including the United States) have willingly given up control over their information to large corporations.

A distributed identity system could allow users to take control of their digital identities. Several governments have already begun to develop distributed forms of digital identities to support their citizens.

The European Union, for example, has started creating a self-sovereign identity framework built on DID and blockchain to modernize government ID for citizens. Countries like Germany, Uruguay, and Finland have started issuing electronic IDs and bank-issued eIDs to serve as national identification.

On a smaller scale, distributed identity can still benefit enterprises internally. By leveraging distributed identity systems, enterprises can connect user IDs with several different service platforms and authorization policies without reinventing or replacing existing identity systems. Additionally, enterprises can then adopt their schemes or extend existing ones offered through government agencies.

Strong Authentication and Distributed Identity with 1Kosmos

Distributed identity isn’t just a powerful new technology or the future of identification—it is a business imperative that will eventually shape how enterprise organizations integrate and adopt different types of managed services, cloud applications, and internal security measures. By working with user-owned, self-sovereign ID, businesses can mitigate some of the most significant weaknesses of centralized identity (security and usability) while expanding their ability to adapt and scale with new technologies.

BlockID from 1Kosmos provides secure authentication and promotes identity ownership through a few critical features:

  • Private and Permissioned Blockchain: 1Kosmos protects personally identifiable information in a private and permissioned blockchain and encrypts digital identities in secure enclaves only accessible through advanced biometric verification. Our ledger is immutable, secure, and private, so there are no databases to breach or honeypots for hackers to target.
  • Identity Proofing: BlockID includes Identity Assurance Level 2 (NIST 800-63A IAL2), detects fraudulent or duplicate identities, and establishes or reestablishes credential verification.
  • Streamlined User Experience: The distributed ledger makes it easier for users to onboard digital IDs. It’s as simple as installing the app, providing biometric information and any required identity proofing documents and entering any information required under ID creation. The blockchain allows these users more control over their digital identity while making authentication much easier.
  • Identity-Based Authentication: We push biometrics and authentication into a new “who you are” paradigm. BlockID uses biometrics to identify individuals, not devices, through identity credential triangulation and validation.
  • Interoperability: BlockID and its distributed ledger readily integrate with a standard-based API to operating systems, applications, and MFA infrastructure at AAL2. BlockID is also FIDO2 certified, protecting against attacks that attempt to circumvent multi-factor authentication.
  • Cloud-Native Architecture: Flexible and scalable cloud architecture makes it simple to build applications using our standard API, including private blockchains.

To discover the self-sovereign identity and BlockID, read more about 1Kosmos as a Distributed Digital Identity Solution. Also, make sure to sign up for the 1Kosmos newsletter to receive updates on 1Kosmos products and services.

Author Robert MacDonald
Virtual Identity