MEV and me: Trustscores for market places

Our project came out the idea to use historic data analysis for trustscores. The idea: To participate in an auction, you have to stake money. If your account participates in executing a sandwich trade. You will loose your stake. Therefore MEV would not be as profitable. Given the timing of zk advances our ultimate goal was to implement Vitalik's latest post on privacy applications and make a soulbound private scoring system.

We started with a negative reputation score by wash trade participation. And ultimately explored both the attack and defense side of NFT marketplace.

We proposed a sniping bot, that would be paid on chain in advance and if the NFT wouldn't get bought in a specific time frame, the money will be returned. We put a lot of effort to make the solidity contract a multi-bidder contract.

Given we understood the nature of sniping bots, we came up with modules that we could attach to SeaPorts latest api. To actually, block these bots. We also learned the hard way to implement zk for the first time is quite complex therefore, we wrote the soulbound token with a public score.

Attack: Sniping Bot:

• x NFTPort for search
• x Reservoir for execution of order
• x Seaport for special forms of auctions
• x Pocket Network for our node
• x React for the frontend
• x TypeScript for backend Defense: Commit Reveal auction for frontrunning protection:
• x Seaport as the auction
• x Solidity for custom smart contract

Washtrading score:

• x The Graph as a subgraph deployed on gnosis, optimsism, polygon and mainnet
• x Python for data analysis

Soulbound Token with private trustscores:

• x WorldCoin Id for identifcation
• x Lens Protocol for reputation increase
• x Member of BitDao for reputation increase
• x Endpoint for scoring system
• x zk circuits with circom (didnt complete and therefore removed)