ZKShield aims to provide a On-chain and Off-chain ZK loyalty proving system through ZKML technology. Our project can verify user loyalty score both on chain and off chain. For on-chain data, this includes factors such as the number of NFTs owned by a specific address, as well as the frequency of interactions between this address and specific contracts or DApps. for off chain data, ZKShield can calculate twitter or other social media score, and use ZK to achieve anonymity. In addition, ZKShield use AI technique to analysis these data like user past transaction data and social credit. Predict the future behavior of this address. Ensure that this user can generate positive development for the project party.
Category
AI Integration
Timeline
Project expected to take 4 months for completion, with an assumed September 1th start date
30 September 2023 : data collection, methodology checking and Start AI model training
30 Novmber 2023 : Finish AI model, Zk, contract building, Start frontend and integration
31 December 2023 : Finish project
Project Plan
We may use Axiom to trustlessly access block headers, accounts, and storage values from any block in the history of Ethereum.
And may use ezkl for doing inference for deep learning models and other computational graphs in a zk-snark. Protect private data
AI technique like Pytorch or Scikit-learn for model training and user behavior prediction
twitter api, web crawling tech for getting off chain data
And build a DApp with frontend technique
Project Impact
In the future, We believe our project can bring benefits to lots of project, DApp, DAO. Can have the following impacts :
Ensure users can actively participate in the governance of the DAO.
Ensure users can be verify reliably when getting airdrop or minting NFT.
Ensure users may have positive impact to certain DApp or projects.
Prevent some malicious users from immediately selling airdrops or NFTs after receiving them.
Project Description
ZKShield aims to provide a On-chain and Off-chain ZK loyalty proving system through ZKML technology. Our project can verify user loyalty score both on chain and off chain. For on-chain data, this includes factors such as the number of NFTs owned by a specific address, as well as the frequency of interactions between this address and specific contracts or DApps. for off chain data, ZKShield can calculate twitter or other social media score, and use ZK to achieve anonymity. In addition, ZKShield use AI technique to analysis these data like user past transaction data and social credit. Predict the future behavior of this address. Ensure that this user can generate positive development for the project party.
Category
AI Integration
Timeline
Project expected to take 4 months for completion, with an assumed September 1th start date 30 September 2023 : data collection, methodology checking and Start AI model training 30 Novmber 2023 : Finish AI model, Zk, contract building, Start frontend and integration 31 December 2023 : Finish project
Project Plan
We may use Axiom to trustlessly access block headers, accounts, and storage values from any block in the history of Ethereum. And may use ezkl for doing inference for deep learning models and other computational graphs in a zk-snark. Protect private data AI technique like Pytorch or Scikit-learn for model training and user behavior prediction twitter api, web crawling tech for getting off chain data And build a DApp with frontend technique
Project Impact
In the future, We believe our project can bring benefits to lots of project, DApp, DAO. Can have the following impacts :
Team Information
Roy - Smart Contract Developer github link : https://github.com/roy105703007
Eric - Full stack Developer github link : https://github.com/NorthBei
Allen - Zk AI Developer github link : https://github.com/abcd5251
Point of Contact
tg: @Allen0915 email: 74865251a@gmail.com
Previous Work
No response
Additional Information
No response
Agreement