Hazel_Whitepaper
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Background
The Internet has realized the efficient transmission of information where the concept of Internet of Everything and its application have greatly changed human life. As the next-generation "Internet of Value", blockchain is expected to deliver and exchange value as accurately, efficiently and safely as information while the digital assets based on the native blockchain system are important carriers in transmission of value.
As an objective measurement of value, price is an important basic component for realizing value exchange. With the rapid development of blockchain, whether value can be exchanged at low cost safely and efficiently has become a core standard for measuring the application value of blockchain. Therefore, as it were, the measurement of application value of blockchain is inseparable from the objective measurement of assets on any chain.
Product Introduction
Hazel is a blockchain-oriented prediction service which features decentralization, or more specifically, a new generation of verifiable oracle machine based on Polkadot. It aims to exactly support the on-chain operation of enterprise-level Internet applications with high-concurrency through consensus mechanisms, smart contracts, trusted computing, privacy protection and the interaction of on-chain and off-chain data.
We expect to provide a data-driven prediction service based on the asset-backed events and a data-correction mechanism based on fact checking. This mechanism could resolve fundamental problems about trusted source of data of blockchain through endorsement of facts.
The realization of effects mentioned above needs to meet 2 basic conditions:
To establish a mapping relationship on price of facts through the pledging of assets
To realize spontaneous correction of event data through market arbitrage
Description of core mechanism
Pledging of assets and data reporting
Users can pledge the two-way assets for predicted events in the contracts through portals such as Dapp and map the on-chain assets to the result of events, thus achieving value anchoring.
In terms of data on asset price, the oracle adopts quotations from miners and generates on-chain prices by real-time correction of on-chain prices originally provided by miners from whole network, thereby effectively avoiding potential risks such as node failures and malignant behaviors of nodes in the process of price feeding of centralized nodes.
Taking DOT/USDT as an example, if we assume that the current market price of 1 DOT is 5 USDT and the scale coefficient of quotation is 1, then the miner needs to send the quotation of assets of 1 DOT and 5 USDT to the on-chain oracle (and pay 0.01 DOT as service charge for quoting). This quotation provided by miners will be verified by any miner in the market. If the current quotation-trading pairs deviate from the market price, the miner mentioned above can send 1 DOT to the on-chain oracle in exchange for 5 USDT, or send 5 USDT in exchange for 1 DOT to achieve arbitrage. The miner will complete the on-chain price correction when achieving arbitrage, and there is no arbitrage space for the corrected quotation, namely, the fair price in the market will be formed.
Data correction
Considering that what the data forecaster predicts may deviate from the facts, the market needs to have enough motivation to correct spontaneously the deviation, that is to say, correcting the data deviation can generate revenue for the corrector and encourage the whole progress to keep going on.
The profit space of the revised data comes from the arbitrage caused by information bias between the on-chain assets locked by the data reporter and the objective value of the assets. The premise of arbitrage is that the arbitrager must also hold certain on-chain assets corresponding to the event data.
Every quotation pair sent to the chain which is deviated from the market price will be exploited by any miner for arbitrage. Miners need to send N times the assets of the quotation pair to be arbitraged when correcting the quotation pair. Assuming that the current quotation pair P1 is 1 DOT / 5 USDT while N=2, then the miner M1, namely the arbitrager, needs to send the quotation pair P2, 2 DOT / 10 USDT, to complete the arbitrage of P1; likewise, the miner M2 needs to send the quotation P3, 4 DOT / 20 USDT, to complete the arbitrage of P2, and so on… The size of quotation asset Pn increases linearly with the frequency of arbitrage C while miners for arbitrage then (no longer pay service charge of quoting) will also no longer enjoys revenue from “mining”.
Final price
With the increasing growth of the on-chain blocks, the oracle will calculate the current effective quotation pair within a fixed time period T. If we assume that the effective quotation pairs include [x1, y1], [x2, y2]… [xn, yn] within the current time period Tn, then the prices quoted during Tn include [y1+y2+...+yn] / [x1+x2+...+xn]. When n = 1, the oracle will calculate the average price of valid quotation pairs in each block during a certain time period and make it as the final price of the block. If there is no quotation from any miner for the current block, then the price of previous block will be used.
Incentives for Mining
Every valid quotation which has been verified by miners from the whole network within a specified period will be rewarded with a certain amount of Tokens for “mining”. The amount of Token rewards is proportional to the scale of the assets which the user pledges when quoting, and 10% of the Token rewards will be taken into DAO for the development of follow-up project. Assuming that the miner M1 who has effectively quoted P0 during the cycle Tn will receive a certain amount of Token rewards for mining, his another valid quotation P1 during the same cycle will also be correspondently rewarded. In conclusion, each transaction of quotation of the same asset size will be rewarded with the same amount of Tokens.
Roles in the ecosystem
Predictor
It refers to the participants who make data predictions for obtaining incentives by pledging assets. In the process of data correction, there may be multiple predictors who will report and predict with fact data.
Corrector
Regarding the arbitrage space due to the information bias between the predicted data and the fact, the corrector can realize market arbitrage through the automatic exchange of the predictor’s assets to be pledged and then report the data again as a predictor. Consequently, the predicted data will be driven to infinitely approach the fact data.
Data consumer
The on-chain data finally adopted by the predictor can be applied in various data usage scenarios in the blockchain ecology including mortgage lending, asset pricing and futures settlement in DeFi (decentralized finance). The data consumer needs to pay a certain amount of Tokens to obtain credible real-time data on-chain of the predicted asset.
Product Features
Fully decentralized
Since the reporting of event data is pledged by real assets, any user on the distributed network of Hazel can involve in reporting data. Compared with the reporting mechanism of multi-centralized nodes that rely on node credit, the endorsement of real assets can achieve more secure data-prediction mechanism to reduce the risk of malicious behavior of node collusion.
Protection of on-chain data privacy
With the large-scale docking of the blockchain and the off-chain world, the range and types of data provided by the oracle will become increasingly more abundant. In terms of high-value data other than price data, the need for privacy protection will become more urgent. Through the collaborative application of TEE, MPC and on-chain contracts, Hazel will realize the privacy protection of high-value data from oracle and complete consequent collaborative calculation as well as application, making it possible that the transaction of value flow under the premise that all parties involved are invisible one another.
Diversity of event data
In addition to releasing price data about traditional cryptocurrencies, Hazel can realize the data prediction towards any event in the off-chain world by issuing on-chain assets linking to any event with pledging assets on the results of events, exactly interconnecting diversified data on-chain and off-chain.
Anti-risk ability
The proposal aims to charge the lowest fees while maintaining CoC (cost of corruption)> PfC (profit from corruption). Tokens which function voting have fundamental value because contract participants will be charged eventually to compensate voters for their work to ensure system security. Therefore, the current value of the token should reflect the present discounted value of all the fees expected to be charged to the contract in the future. The market value τ of tokens (measured by the price multiplied by the supply or pτSτ) should be equal to the sum of all expected repurchase fees Ft charged on PfCt in the future at the discount rate r:
Since any fee Ft is a function of the total value guaranteed by the system during the time period t, the market's expectation for all fees in the future is a reflection of the market's expectation for the growth of the total value guaranteed by the system. It means that if the usage of the system expected by the market actually grows, the price of tokens at present should reflect this expectation. It also implies that the price of tokens p can be maintained above the required psafe level without charging any fees initially. In other words, the inequality of CoC>PfC can be maintained entirely in accordance with growth expectations. If the usage of the system does not increase as expected, fees will eventually be charged in a stable state in the future. However, this structure is intellectual in that when the system's growth is expected to be greatly high, no fees or minimum fees will be charged at the initiate stage of the system, which should help encourage early adopters to use the system. In the stable state in the future, when the growth of usage of the system slows down or stops, users will be compensated for voting with their capital costs (the cost of buying and holding voting tokens).
Product roadmap
2021 Q1
Development of Hazel testnet
1. Complete the initial link between Hezel and Polkadot with a smart contract. The permissions for users incorporate checking and managing personal services and account information, etc.
2. Create tools such as exchange/protocol codes for formats, standardization and so on.
3. To ensure that the system will operate as expected, we will conduct multiple internal audits throughout the development cycle.
2021 Q2
Release of Testnet
1. Deploy key smart contracts in the testnet so that the connectivity of the smart contract can be successfully delivered.
2. Multiple tests will be created for the entire process of operation to ensure safety and reasonableness of the whole structure.
3. Open Hezel's external test tasks and deliver codes to the third-party auditor for reviewing.
Main network available
1. Release the full version of the main network service and launch Hezel.
2. Issue tasks on hacker bounty, the function of analysis will be supported by the community to assist the development of core products.
Dapp available
We will use Hazel-based contracts to create Dapp so that any user who is willing can participate in it.
Products include well-matched back-end and front-end, which support iOS, Android, and WEB.
2021 Q3
1. The product for consulting service Blockchain Forest will be connected to Hezel data service.
2. More partners in the blockchain ecology will be introduced and accessed to.
Last modified 10mo ago
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