In the Web3 ecosystem, the 'imbalance in value distribution of on-chain data' has become a key contradiction that restricts the long-term development of the ecosystem: Users, as the core producers of on-chain data (transaction records, asset holdings, ecosystem interactions, etc., all stem from user behavior), can hardly benefit from the value of their data—data is used by platforms for tool development, algorithm optimization, and attracting business cooperation, with profits going entirely to the platform. Users can only use basic data services for free and even face risks of data privacy breaches. At the same time, users’ on-chain data (such as credit records, trading preferences, and ecosystem contributions) cannot be monetized like physical assets due to 'lack of rights recognition and difficulty in circulation', becoming 'sleeping value'. Traditional on-chain tools only play the role of 'data movers', failing to address 'value distribution' and 'assetization', leading to an imbalance where 'data producers suffer while platforms profit'. The core breakthrough of Bubblemaps lies in constructing a full-link system of 'data rights recognition—privacy protection—value distribution—asset circulation', allowing users to truly control the ownership, usage rights, and profit rights of on-chain data, shifting Web3 data from 'platform monopoly value' to 'user-led value', achieving 'data contribution equals profit'.
1. The three core pain points of Web3 data value distribution.
The value contradiction of Web3 data essentially stems from the misalignment of 'data ownership, usage rights, and profit rights', specifically manifested in three major pain points that directly restrict user participation enthusiasm and ecosystem health:
(1) Value monopoly: data profits belong to the platform, and users are merely 'free contributors'.
The commercial value of on-chain data is mainly reflected in three scenarios: 'tool development, ecological analysis, and business cooperation': platforms aggregate user data to develop paid data tools (such as institutional-level data analysis services), provide user profiling and ecological insights to project parties (charging consulting fees), and access third-party applications (gaining API call revenue), but almost all these profits go to the platform. Users, as the source of data, have neither profit-sharing rights nor the right to know. For example, a certain platform developed a 'cross-chain arbitrage strategy tool' based on users' cross-chain transaction data, earning over a million dollars a year, while the users providing the transaction data received no compensation; a certain project party purchased 'user holding data in a certain sector' from the platform for precise marketing, with the fees going to the platform, while users were unaware that their data was being used commercially. This model of 'user contribution, platform profit' is essentially an 'exploitative distribution' of data value, which will lead to user resistance to data sharing in the long term and restrict the richness of the data ecosystem.
(2) The conflict between privacy and utilization: 'wanting to benefit but fearing leakage'.
Users are not unwilling to share data; rather, they are concerned about 'data misuse and privacy breaches': sharing sensitive data such as transaction records and asset sizes to gain profits may expose them to 'identity information association, asset security risks, targeted fraud', etc. If users refuse to share to protect privacy, they cannot convert data into actual value. Traditional tools either 'fully open data (no privacy protection)' or 'fully encrypt data (unable to utilize)', lacking a 'controllable anonymity' balanced solution—for instance, users wish to obtain low-interest loans through 'credit data' but do not want the lending platform to know their specific asset addresses and transaction history; they want to gain DAO voting weight through 'ecosystem contribution data' but fear their contribution records will be used for targeted service push. This 'dilemma' prevents a large amount of high-value data from entering circulation due to privacy concerns, missing out on value conversion opportunities.
(3) Low degree of assetization: data is difficult to become 'circulable assets'.
Users' on-chain data has 'uniqueness, scarcity, and practicality', meeting asset attributes, but due to 'lack of rights recognition, difficulty in segmentation, and no trading scenarios', cannot circulate and monetize like NFTs or tokens: Users’ 'long-term compliant trading records' (which can prove credit), 'deep contribution data in a certain ecosystem' (which can obtain rights), and 'precise trading strategy data' (which can guide investment) all have value but cannot mark 'ownership' and cannot be traded in the market. Even if some platforms attempt to monetize data, they mostly adopt 'centralized pricing' (such as platforms acquiring data at fixed prices), with users lacking bargaining power, and after data transactions, subsequent usage scenarios cannot be traced, making it easy to be resold multiple times. This 'non-assetization of data' situation prevents users' 'data labor' from converting into 'asset appreciation', weakening the long-term motivation for ecosystem participation.
2. Data rights recognition and privacy protection: building a user-led value foundation.
To solve the problem of data value distribution, the primary prerequisite is to 'clarify data ownership' and 'protect data privacy'—only when users confirm that 'data belongs to them' and 'privacy is guaranteed' will they be willing to participate in value distribution. Bubblemaps builds a user-led data security foundation through 'on-chain rights recognition + zero-knowledge proof + fine-grained authorization', ensuring clear data ownership while achieving 'controllable utilization and non-disclosure of privacy'.
(1) On-chain data rights recognition: using 'data NFTs' to mark ownership.
Bubblemaps generates 'data NFTs' for users' on-chain data, serving as the only proof of ownership—each type of users' core data (such as 'Ethereum 6-month transaction records', 'Polygon ecosystem contribution data', 'cross-chain asset allocation data') generates corresponding NFTs (based on ERC-721/ERC-1155 standards) after user authorization. The NFT metadata includes 'data hash value' (which uniquely identifies the data), 'data type', 'generation time', and 'user address', with the NFT being recorded on-chain to ensure it is tamper-proof. Users holding data NFTs thus possess the 'ownership' of that data—being able to decide whether to share the data, how to share it, and how much profit to obtain. Any platform or project party using the data must obtain authorization from the NFT holder (the user), and unauthorized use can be traced back through on-chain records. For example, after a user's 'compliant staking record of a certain DeFi protocol' generates a data NFT, only that user can authorize the lending platform to use this data to obtain lower interest rates; the platform cannot unilaterally access or resell it.
Data rights recognition does not mean 'putting the original data on-chain' (to avoid privacy breaches), but 'putting the ownership certificate of the data on-chain'—the original data is still stored in encrypted form, only associated with the data hash value and NFT, ensuring clear ownership while protecting the privacy of the data content.
(2) Zero-knowledge proof: achieve 'data usable but invisible'.
To address the 'conflict between privacy and utilization', Bubblemaps introduces Zero-Knowledge Proof (ZKP) technology, allowing data to be verified and utilized without exposing the original content. For example:
• A user wants to prove to a lending platform that 'they have no overdue repayment records in the past three months' (credit standard), without providing specific repayment addresses and amounts, just generating a 'credit standard certificate' through zero-knowledge proof—the platform can verify the validity of the certificate (confirming the user meets the conditions) but cannot obtain any original transaction data.
• A user wants to prove to a DAO that 'their contribution value in a certain ecosystem exceeds 1000 points' (which can obtain voting rights), without disclosing contribution details (such as proposals participated in and interaction frequency), just generating a 'contribution value standard certificate', which the DAO can validate to grant corresponding rights, while the original contribution data remains private to the user.
The core value of zero-knowledge proof is to break the misconception that 'data utilization must expose content', allowing users to achieve 'scenario-based verification' of data while protecting their privacy, paving the way for subsequent value distribution and assetization.
(3) Fine-grained authorization: users control 'data usage boundaries'.
To avoid 'one-time authorization leading to unlimited data usage', Bubblemaps designs a 'fine-grained authorization mechanism', allowing users to precisely control the scope, duration, and scenarios of data usage:
• Range control: During authorization, users can choose to 'share only certain fields of the data', for example, sharing 'total asset size' with exchanges but not sharing 'specific asset types'; sharing 'total contribution duration' with DAOs but not sharing 'specific contribution behaviors';
• Duration control: Set an authorization validity period (such as 7 days, 30 days), after which the authorization automatically becomes invalid, and the platform cannot continue using the data without reapplying for authorization.
• Scenario control: Limit the usage scenarios of data (for example, 'only for lending credit evaluation' or 'only for DAO rights determination'); if the platform uses the data for other scenarios (such as targeted marketing), the system will trigger an alert and terminate the authorization.
All authorization records are kept on-chain, allowing users to view in real-time via the 'data authorization dashboard' 'who is using the data, usage scenarios, remaining duration', and to revoke authorization at any time, ensuring that data usage is always within their control.
3. Dynamic value distribution mechanism: returning data profits to contributors.
After clarifying data ownership and privacy protection, the core is to establish 'fair value distribution rules'—to allow users to obtain reasonable profits based on data contribution, rather than being unilaterally decided by the platform. Bubblemaps constructs a dynamic and transparent value distribution system through 'contribution quantification + smart contract profit sharing + community governance'.
(1) Quantifying data contribution: calculating profit weight by 'value density'.
Different types of on-chain data have significant differences in commercial value and ecological value: 'long-term compliant trading records' (credit data) are more valuable than 'single transfer records' (ordinary data), while 'deep contribution data in a certain ecosystem' (scarce data) are more valuable than 'general browsing data' (common data). Bubblemaps quantifies data contribution through a 'value density model', assessing it from three dimensions:
• Scarcity: The rarity of data in the ecosystem (for example, 'developer data from a niche public chain' is scarcer than 'ordinary user data from Ethereum', hence has a higher weighting).
• Practicality: The application value of data in commercial or ecological scenarios (for example, 'data that can be used for credit evaluation' is more practical than 'data used solely for statistics', hence has a higher weighting);
• Compliance: Whether the data meets global regulatory requirements (for example, 'data linked to completed KYC is more compliant than 'anonymous data', hence has a higher weighting).
Contribution measurement results are stored on-chain as the core basis for profit distribution—data with higher contributions will yield a higher profit share for users, avoiding a one-size-fits-all average distribution and ensuring that 'high-value data brings high returns'.
(2) Smart contract automatic profit sharing: profits arrive in real-time, transparent and traceable.
Bubblemaps automatically distributes commercial benefits from data (such as API call fees, data consulting fees, tool subscription fees) through smart contracts without manual intervention, with distribution rules being publicly transparent:
• Basic profit-sharing ratio: By default, 70% of profits go to data contributors (users), 20% to data verification nodes (to ensure data authenticity), and 10% to the protocol community (for technical iteration and ecosystem maintenance);
• Dynamic adjustment: If the 'reuse frequency of data exceeds 100 times' (high-value data), the contributor's profit share can be increased to 80%; if the data contains 'some invalid information' (low-quality data), the profit share can be reduced to 50%, with adjustment rules decided by community proposal voting.
• Real-time arrival: When data is used and generates profits, the smart contract immediately transfers the corresponding share of profits (in stablecoin or platform token form) to the user’s wallet, with profit distribution records on-chain, allowing users to view 'the source of each profit (which platform used the data), amount, time' at any time to ensure no hidden operations.
For example, if a user's 'compliant credit data' is accessed by three lending platforms, generating 10 USDT in profit each time, at a 70% profit-sharing ratio, the user receives 7 USDT each time, totaling 21 USDT for three accesses, with profits arriving in real-time and the platform information for each access traceable.
(3) Community governance: Let the distribution rules be 'decided by the ecosystem'.
The value distribution rules are not unilaterally set by Bubblemaps, but dynamically optimized through 'DAO community governance':
• Proposal initiation: Any user or node can initiate proposals for 'profit-sharing ratio adjustment' or 'contribution model optimization', requiring specific logic and data support (for example, 'suggest increasing the profit-sharing ratio for compliant data due to a 30% increase in demand for compliant data in institutional cooperation');
• Voting: Users and nodes holding platform governance tokens can participate in voting, with voting power linked to 'data contribution + token holdings', ensuring high contributors have more say;
• Rule effectiveness: Proposals automatically update to smart contracts after receiving more than 50% voting support; new rules take effect immediately, with all proposals and voting records on-chain to ensure public transparency.
Community governance avoids 'platform dictatorship', making value distribution rules more aligned with ecosystem needs, maintaining the fairness and sustainability of the distribution system in the long term.
4. Data assetization circulation: creating a closed loop from data to assets.
Generating profits from data is just the first step; the more core issue is to achieve 'data assetization and circulation'—allowing users' on-chain data to be traded, pledged, and mortgaged like NFTs and tokens, forming a complete closed loop of 'data—assets—profits' to maximize data value.
(1) Decentralized trading of data NFTs: users set prices autonomously and circulate freely.
Bubblemaps builds a 'decentralized trading market for data NFTs', where users can list their held data NFTs for sale or auction, autonomously deciding on the price and trading method:
• Listed transactions: Users set a fixed price for data NFTs (such as 1 ETH), and other users can purchase directly. After the transaction completes, data ownership (data NFT) and usage rights (attached authorization) are transferred simultaneously, with the original data safely delivered through zero-knowledge proof technology to avoid buyers obtaining it for secondary sales.
• Auction transactions: For high-value data NFTs (such as 'historical backtesting data of a certain quantitative strategy' or 'core user profile data of a certain ecosystem'), users can initiate auctions, set a starting price and auction duration, with the highest bidder receiving the NFT. The entire auction process is recorded on-chain to ensure fairness.
• Transaction assurance: Introduce 'data verification nodes' to verify the authenticity of listed data NFTs (such as verifying whether the data hash value matches the original data, and whether there are compliance risks), allowing listing only after passing verification to avoid 'false data NFT' transactions.
For example, a user's 'historical arbitrage strategy data of a certain DeFi protocol' (annualized return of 25% backtested) generates a data NFT, which is listed for 2 ETH in the trading market, purchased by an institutional user. The user receives 2 ETH in profits, and the institution obtains strategy data for investment optimization, achieving 'mutual matching of data value'.
(2) Scenario-based monetization of data NFTs: not just trading, but also 'activating' them.
In addition to direct transactions, Bubblemaps also supports 'scenario-based monetization' of data NFTs, enabling data assets to be fully utilized within the ecosystem:
• Pledge financing: Users can pledge data NFTs to lending platforms to obtain stablecoin loans (pledge rates are determined based on data value, such as high-value data with a pledge rate of 50%, ordinary data with 30%), repay the loan and pay interest upon maturity to redeem the NFT.
• Binding rights: Data NFTs can be bound to ecological rights, for example, holding a 'certain public chain developer data NFT' could grant priority application rights for 'developer subsidies' or 'whitelist for new project private placements', increasing the added value of data NFTs.
• Rental authorization: Users can 'rent' data NFTs to platforms or project parties, collecting rent (for example, charging 0.1 ETH monthly). During the rental period, the other party only obtains usage rights (as per the authorization scenario), while ownership remains with the user, and usage rights are reclaimed after the rental period ends.
This 'multi-scenario monetization' model allows data NFTs to no longer be 'one-time transactional assets' but 'long-term activatable appreciating assets', further amplifying users' data value profits.
5. Ecological value: reconstructing the value logic of Web3 data.
Bubblemaps' 'data value distribution and assetization' system is not simply a 'user revenue-sharing tool', but a fundamental reconstruction of the value logic of Web3 data—transforming data from 'the core asset of the platform' into 'users' personal assets', from 'monopolistic resources' into 'circulable production factors', bringing three core values to the Web3 ecosystem:
For users, it allows 'data contribution' to truly convert into 'actual profits', with users no longer being 'free data laborers', but 'owners and beneficiaries of data assets', enhancing both the enthusiasm for ecosystem participation and trust in Web3; for the ecosystem, it activates a large amount of 'sleeping high-value data' (data that has not been shared due to privacy concerns or lack of profits), allowing data to circulate under compliance and safety, providing richer data support for tool development, project iteration, and institutional cooperation, enhancing ecosystem efficiency; for the industry, it provides a feasible Web3 solution for 'data value distribution'—different from Web2's 'platform monopoly', it is based on blockchain's 'transparency, fairness, and user-led', offering a decentralized model for global data governance.
Conclusion
The core vision of Web3 is to 'return to user sovereignty', and data sovereignty is an important part of user sovereignty. The value of Bubblemaps lies in its ability to truly return Web3 data to user ownership, controlled by users, and benefiting users through the full-link system of 'rights recognition—protection—distribution—circulation'. When every piece of users' on-chain data can be recognized, protected, fairly distributed, and flexibly monetized, Web3 can truly escape the shadow of 'data monopoly', achieving a positive cycle of 'data-driven ecosystem, ecosystem feeding back to users'. This is not a fictional ecological ideal, but a practical landing of existing Web3 technologies such as zero-knowledge proof, NFTs, and smart contracts, as well as the inevitable direction of Web3 from 'technological innovation' to 'value fairness'.@Bubblemaps.io
