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Decensus

Decensus is an incentivized decentralized survey for NFT collections with privacy protections. An NFT community gains insights about their own community by answering the questions. In this demo, the incentive is the data insight itself like Level.fyi.

Decensus

Created At

Metabolism

Winner of

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🥈 Lit Protocol — Best Use

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🏆 Metabolism Finalist

Project Description

This project solves the oft-encountered pains of NFT community to collect information from their community members. NFT community, especially the organizers, would love to learn about their community members in order to develop their projects to engage members even more or identify diversity gaps. For example, they want to understand the demographics (countries they live in, age-segments or gender), preferences of future projects (designs of future NFTs, incentives they enjoy, location of IRL events) as well as insights into what other NFT projects people like for alphas and collaboration potentials. However, currently we don’t have great tools to collect people’s information or opinions in a privacy-safe and effective way. Another big issue with the current survey in Web2 is the lack of motivation for users to answer them.

With decensus, any NFT holder can initiate surveys to start collecting survey answers for a given NFT collections, specified by contract address. NFT holders for a given NFT collection answer the surveys through an NFT-gated page, and their answers are stored with encryption, with no identity attached. We incentivize members to answer these questions by rewarding them with Tokens or NFTs or data insights once the survey answers are provided. In this demo, we reward members with statistical data insights about the community. Just like Levels.fyi, we believe there are questions people are curious enough to collaborate on this effort. In addition, the psychological barrier to answer the survey is minimized through gated survey access with NFT, anonymization of data by wallet address stripping, aggregation of data in data insights and provision of statistical data to only those who answer questions.

How it's Made

We effectively utilized Lit Protocol in two places; 1. Encrypting the survey forms and put access control with NFTs, 2. securing and encrypting survey answers, and decrypting and displaying the statistical data insights from the survey data only if the wallet has answered the survey question themselves. For encryption of survey answers, we used the integration of Lit protocol and Ceramic. Since this integration requires DID to be signed for storing information in Ceramic, users will have to sign in with DID.

A smart contract was created in order to confirm the on-chain cryptographic proof of survey answers. We could also issue NFTs as a proof to be owned by those who answer surveys, but this approach is simpler with less gas usage and prevents people from transferring the rights to view the data. If more financial reward is more appropriate, then one can easily change this smart contract to issue NFT or FT.

Statistical data is pulled from Ceramic with the decryption enabled by accessing on-chain proof in smart contract. This approach is more secure than the token-gated URL of the data insight page, since survey data is pulled only if the wallet accessing the data is approved by Lit Protocol.

This demo is only an initial point of our general concepts for developing web3 native systems to collect data. A few areas of improvements are combinations of the following:

  1. Extend the incentive reward mechanisms to NFTs and FTs
  2. Make it flexible to create and choose different questions in the survey.
  3. Enable pivot-table like analysis by combining survey data with on-chain data, while preserving the privacy of wallets (idea is to join the tables in the server but discard the wallet address used to join the tables, so that people can be sure that their privacy is protected),
  4. Increased insight details of data by automatically extracting data from known credentials through APIs (social connect etc)
  5. Extend this to include data labeling and cleaning efforts similar to mechanical-turk like crowd-sourcing
  6. Include ZKProof for better privacy protection in the submission of data.
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