Web3 draws a beautiful vision of how the new internet should be. The overturn of hierarchy, the rebalanced dynamic between convenience and control, the dismantling of surveillance capitalism. Though web3 laid a solid ground for decentralized storage, the system is also flawed and immature, with scattered, static data laying throughout the network. In real life, various identity data are generated simultaneously around the world, which might seem irrelevant and disparate, but as well complicatedly intertwined and when gathered could be of great use.
To curb this challenge, Litentry is providing an interchangeable tool of identity data. As a decentralized identity protocol built on Substrate, Litentry allows users to collect identity data across blockchains and manage them in one place.
✨👀 Blockchain identity management protocol | Litentry
🌏 Identities Integration
With Litentry, users collect their identity data from third-party applications and manage it in a dAPP. Identity data are validated and processed by Identity Guardians and will be securely stored in decentralized storage such as IPFS. This encrypted, decentralized storage is a vast security improvement over proprietary centralized database, which has been infamously compromised in the past. Given that data from different platforms are often unorganized and incompatible, the Data Resolver comes into place by organizing and unifying them in a consistent, compatible form.
Litentry proposes a one-ID-for-all system to secure identity information and change the cumbersome scheme of multiple accounts for multiple services. The product, in their own words, “enable[s] Single Sing-On and Proxy Payment.” After the user generates one account on Litentry, s/he can simply use that account in other applications. The system eliminates the tedious process of creating, managing, and logging in to different accounts. The experience of registering in new service providers will be smoothed out while Litentry’s identity authenticator handling the work for both ends.
đź’Ś Identities Matching And Sharing
When a user accesses a new platform, s/he can choose to grant the new application access to their data library in exchange for better service. When used properly, these data can make a new application more appealing to users, because data help predict user interests and preferences, analyze behaviors, optimize recommendations, and even train and improve machine learning models. Accurately grasping user intents or interests means a better user experience. Ex., migrating music listening history and activities to Spotify so as to enhance and extend sphere of experience. In terms of academic purposes, Litentry can also anonymize and aggregate data more efficiently so that researchers can scour data sets for patterns.
The aforementioned scenario implements the Identity Matching Mechanism. Generally speaking, the Litentry network takes in a third party’s matching request, selects qualified DIDs from the identities pool, returns the result and notifies identity owners whose DIDs were chosen. This mechanism is well designed to support a wide range of activities as below:
- Dating, C2C sales, professional-skill-oriented service
- Curating data for business analysis reports
- Identifying E-commerce target audiences
🛍 The Data Marketplace
Litentry served as an efficient data marketplace where users give others permission to access their data and ask them to pay for it. Users might voluntarily expose their information when necessary, and conversely, commoditize part or all of their data. In this case, the majority of fees that data buyers paid goes back to users, while the network charges a gas fee.
As a neutral, decentralized service provider, Litentry doesn’t make the decision when it comes to data sharing. It’s totally up to users to share their personal information. Meanwhile, Litentry is paying careful attention when designing a data marketplace that well protects and benefits the users. People do care about privacy, but they can behave inconsistently because of “information avoidance”, according to a research paper that investigates the privacy paradox by Dan Svirsky, a graduated researcher at Harvard. Insofar as people have revealed preference and are ignorant to hidden information, more thoughts should be given to user interface design and the infrastructure in data privacy.
đź’« The Credit System
In the credit system, a user sends a request to the network for credit computation, then a credit score would be generated by off-chain workers in real-time. This system is useful in DeFi lending, where individual borrowers provide their identities and credit information to a lending platform in an attempt to get a loan. With a trustless, decentralized authentication, lending platforms save themselves the trouble of setting up an identity-authenticating system from scratch.
The team promotes diversity in their identity credit system. They believe in a pluralistic evaluation system that holds multi-dimensional views of an individual. “We believe that the world is a diverse place where people’s opinions vary,” the founder said, “a widely used algorithm is ought to be changed by major stakeholders and the majority of the voting power.” Being said, their credit computation algorithm and computing process is transparent and trustless. They are also open to hearing different voices from the stakeholders and the community in an effort to constantly improve the functions they use.
Our online identity is highly fragmented. The need for identity aggregation and control is growing at a geometric rate. Though there are an increasing amount of companies that provide alternative options, I believe that Litentry has the most scalable and promising product. Moving forward, they will enable users to control identity data in an active, informed way and truly capture the value of their own data.
