Thread by @pickle2077

This is a thread of dApp ideas for #OasisNetwork. Not all of them are distinct (i.e. some ideas can be combined). Please feel free to reply with additional ideas. I will be adding to this thread over time. $ROSE

1) Undercollateralized or variable rate lending. Borrower reputation can be established via private data such as credit history. Those with good reputation can get loans using less collateral and/or loans with lower interest rates.

2) MEV-resistant DEXs. Important aspects of orders such as slippage parameters can be kept private from even the nodes themselves, preventing them from manipulating orders and/or frontrunning. This allows users to keep more of the value of their trades.

3) Games with hidden on-chain state. Example: puzzle games in which there is an answer that must be kept from the player. Another example: games in which players act on information that cannot be revealed to other players.

4) Games that rely on random number generation. Oasis can use TEEs to generate random numbers without an external oracle. Gambling games could make use of this (rolling a die). RPGs could incorporate this as well.

5) Privacy-preserving decentralized identity. Also called anonymous credentials. This could allow people to, for example, satisfy KYC/AML laws without revealing their entire identity to everyone. This can also allow dApps to screen users based on e.g. age or country.

6) Confidential NFTs. First implemented by @Auth3MetaMirror. The owner of an NFT gains access to private data such as an image. This could be used to enhance the value of owning an NFT since not every aspect of the token is available publicly.

7) Confidential voting. This could be especially useful for DAOs. It allows for votes to be held in which voters are not influenced by the votes that were submitted before them.

8) More fair auctions. VCG, second price and candle auctions are examples of more fair auction methods that require privacy.

https://twitter.com/OasisProtocol/status/1580923496795758592

9) Collaborative analytics. For example, multiple companies or individuals could pool their data and perform a privacy-preserving analysis whose results are only shared within a certain audience. Since data is only analyzed within a TEE, there is less chance for a data leak.

10) Prediction markets for crowdsourced data. Imagine sending out a survey to people asking a list of questions. After everyone submits the survey, they can bet on the results of this survey. E.g. "What is the most popular response to X? How do you predict X to relate to Y?"

11) Biomedical data marketplace. Biometric data from wearing a smartwatch, demographic data, genomic data, blood work data, etc. all could be uploaded and protected so that only owners of the data can control who accesses it. Data analysts could pay data owners for access.

12) Decentralized, anonymous competitions. For example, a writing contest can be held where contestants' anonymized entries are given to a panel of anonymous judges randomly selected from a pool of qualified judges. The past judging history of a judge can be used to select them.

13) An info leak marketplace. Leakers can upload private information. The leaker can choose to sell it to one, several, or many people. They can also set a fundraising threshold past which they will reveal the secret to everyone. Leaker reputation can be based on ratings.

14) Proof-of-humanity. A privacy-preserving user verification system can be implemented that proves a user is human based on uploaded ID documents or attestations from trusted institutions/companies. This could be useful on any dApp that wants to exclude bots.

15) Family Tree DAO. A type of Data DAO that stores family information like genome sequences, photos/videos and medical records. Data can be kept private within the family and passed down through the generations.

16) Initial Data Offering (IDO). Any endeavor with the potential to yield valuable data could sell rights to future data revenue in order to raise funds. Studies, polls, surveys, Data DAOs, new data-generating products or applications, etc. could all raise money this way.

17) Data science competitions for private data. This would be a decentralized version of Kaggle, which allows people to offer datasets that competitors can analyze in order to win reward money. For private data, all analyses would be performed within a TEE, preserving privacy.