Borrower Reputation

The Digital Dock on-chain reputation system relies on a meticulously designed data aggregation layer to construct a real-time financial profile for credit assessment. This layer integrates and standardizes data from heterogeneous sources, employing a combination of blockchain network interaction and traditional banking APIs.

Core Components and Functionalities

• Blockchain Data Fetcher: This component acts as a bridge between the system and various blockchain networks. It retrieves relevant data through custom-designed functions for each supported chain.

  • Ethereum Integration:

    • The getEthereumData(blockHash/transactionHash) function serves as the primary interface for fetching Ethereum blockchain data. It accepts either a block hash or a transaction hash as input and returns a JSON object containing pertinent details like transaction value, gas used, and timestamps. This function leverages the Web3.js library to interact with the Ethereum network. Web3.js functionalities like web3.eth.getBlock and web3.eth.getTransaction are employed to retrieve block data and specific transaction details respectively. Additionally, real-time event tracking is achieved through subscription to relevant events using web3.eth.subscribe.

  • Polygon Integration: Similar to Ethereum, a dedicated function getPolygonData(transactionHash) is designed to interact with the Polygon network. This function takes a transaction hash as input and returns a JSON object encapsulating data such as validator performance and transaction fees. Integration with the Polygon network is facilitated by the Polygon SDK, which provides access to staking information and enables transaction processing through the Polygon Web3 API.

  • Solana Integration: The getSolanaData(transactionSignature) function caters to data retrieval from the Solana blockchain. It accepts a transaction signature as input and returns a JSON object containing relevant data points like block time and transaction fee. Interaction with the Solana network is achieved through the Solana Web3.js library. This function leverages the solanaWeb3.getTransaction function to fetch transaction details by their unique signature. Solana's high transaction throughput is harnessed to ensure efficient data acquisition.

• Banking API Connector: This component bridges the gap between the system and traditional financial institutions, facilitating the retrieval of user-authorized financial data.

  • The getBankData(userId, accessToken) function serves as the central point of communication with bank APIs. It takes a user ID and an access token as input and returns a JSON object encompassing retrieved financial data such as account balances and transaction history. Secure access to user data is ensured through the utilization of integration platforms like Plaid, which offers access to a broad range of financial institutions, and Open Banking API, which promotes user-permissioned data sharing compliant with regulations like PSD2.

Real-Time Tracking and Data Extraction

Beyond core data retrieval functionalities, the data aggregation layer incorporates mechanisms for real-time tracking and data extraction.

• Real-time Tracking: To capture the dynamic nature of blockchain activity, a custom smart contract event listener is developed. This listener monitors complex smart contract interactions and DeFi activities across supported blockchains, enabling the system to react to on-chain events in real-time. Additionally, a state channel communication protocol can be implemented to facilitate low-latency, real-time data exchange between the blockchain and the data aggregation layer.

• Data Extraction: The extractTransactionData(transactionObject) function serves the purpose of extracting relevant data elements from raw blockchain transaction data represented as JSON objects. This function parses the input object and returns a new JSON object containing extracted data points such as gas fees, timestamps, transaction values, and wallet addresses.

• Address Tagging: To categorize and understand the behavior of blockchain addresses, an address tagging function is implemented. This function, denoted as tagAddress(address, behaviorPattern), takes a blockchain address and a behavior pattern string as input. The behavior pattern string represents a characteristic pattern of transaction activity associated with a particular address type (e.g., exchange, DeFi wallet). Based on the analysis of the address's transaction history, the function assigns a corresponding tag to the address, enriching the context of the retrieved data.

Data Normalization and Anomaly Detection

The data aggregation layer incorporates mechanisms to ensure data quality and consistency before feeding it into the creditworthiness score calculation engine.

• Data Normalization Module: A schema conversion function, denoted as convertToStandardSchema(rawData), is employed to transform raw data obtained from various blockchains into a unified JSON format adhering to a predefined standard schema. This standardization process ensures consistent data processing throughout the system.

• Anomaly Detection: To identify potentially fraudulent transactions, outlier detection algorithms like Isolation Forest or Local Outlier Factor are implemented. These algorithms analyze transaction data and assign an anomaly score to each transaction based on its deviation from the expected behavior patterns. This allows the system to flag suspicious activity and improve the integrity of the creditworthiness assessment process.

The meticulously designed data aggregation layer, with its diverse functionalities and robust data processing techniques, lays the groundwork for the reputation calculation engine. This engine leverages the aggregated financial data and blockchain activity information to generate a user's creditworthiness score.

Reputation Calculation Engine: Algorithms and Scoring Mechanism

The reputation calculation engine constitutes the core analytical component of the system. It ingests the processed data from the data aggregation layer and employs a sophisticated algorithm to compute a user's creditworthiness score.

• Creditworthiness Score Function: We define a mathematical function calculateReputationScore(loanRepaymentScore, financialStabilityScore, blockchainActivityScore) that represents the core reputation calculation process. This function takes three arguments, each corresponding to a sub-score component, and returns a single numerical value representing the overall creditworthiness score.

  • Sub-Score Components:

    • Loan Repayment Score (LRS): This sub-score reflects a user's repayment history and is calculated using the calculateLoanRepaymentScore(paymentHistory) function. The paymentHistory argument encompasses a record of the user's past loan repayments, including on-time payments, late payments, and defaults. The function assigns points based on the timeliness of repayments, with on-time payments receiving positive points and late payments/defaults incurring penalties. The maximum achievable LRS is capped at a predefined value (e.g., 500 points).

    • Financial Stability Score (FSS): This sub-score assesses a user's overall financial health and is computed using the calculateFinancialStabilityScore(incomeData, expenditureData) function. The incomeData and expenditureData arguments represent the user's income consistency, expenditure-to-income ratio, and savings rate. The function analyzes this financial data and assigns points based on factors like consistent income, low expenditure relative to income, and high savings rate. The maximum achievable FSS is also capped at a predefined value (e.g., 300 points).

    • Blockchain Activity Score (BAS): This sub-score evaluates a user's level of engagement with blockchain activities and is calculated using the calculateBlockchainActivityScore(transactionFrequency, assetDiversity, smartContractInteractions) function. The function considers the frequency of the user's blockchain transactions, the diversity of asset types held in their wallets, and the frequency of their interactions with smart contracts. Higher engagement in these activities generally translates to a higher BAS. The maximum achievable BAS is capped at a predefined value (e.g., 200 points).

• Weighted Average Score: The calculateReputationScore function combines the individual sub-scores using a weighted average approach. Each sub-score component is assigned a weight based on its relative importance in determining creditworthiness. Traditionally, loan repayment history holds the most significance; thus, the LRS weight (α) is typically higher than the weights for FSS (β) and BAS (γ). The final creditworthiness score is calculated as follows:

Reputation Score = α * calculateLoanRepaymentScore(paymentHistory) + β * calculateFinancialStabilityScore(incomeData, expenditureData) + γ * calculateBlockchainActivityScore(transactionFrequency, assetDiversity, smartContractInteractions)

This mathematical framework ensures a data-driven and transparent creditworthiness assessment process. By incorporating a combination of traditional financial data and blockchain activity metrics, the system provides a holistic evaluation of a user's financial profile.

Dynamic Reputation Updates with ZK-STARKs

To preserve user privacy while enabling verifiable reputation updates, the system leverages Zero-Knowledge proofs (ZKPs) based on the STARK (Scalable Transparent ARKs) cryptographic scheme.

• ZK-STARK-based Credential Generation: When a user's creditworthiness score undergoes a significant change due to new data (e.g., a loan repayment or a new DeFi interaction), a ZK-STARK proof is generated. This proof, denoted as generateZKProof(scoreCategory), takes the user's new creditworthiness score category (e.g., "Green", "Yellow") as input. The ZK-STARK proof generation process mathematically proves that the user's score falls within a certain category without revealing the underlying details of their financial data or individual transactions. This cryptographic technique ensures user privacy while maintaining the verifiability of the reputation score update.

• Dynamic Update Protocol: The system implements a dynamic update protocol to handle ZK-proof generation and verification. This protocol includes the following functionalities:

  • Event Monitoring: Continuously monitors credit-related events on each supported blockchain to detect any changes that might affect a user's creditworthiness.

  • Triggering ZK-Proof Update: Upon detecting a significant change in a user's credit data, the protocol triggers the generation of a new ZK-proof using the generateZKProof function.

  • Update Verification and Score Update: The generated ZK-proof is disseminated to relevant entities within the system, such as loan providers or DeFi platforms. These entities can then verify the authenticity and validity of the proof using a verification function denoted as verifyZKProof(proofData). This function takes the received ZK-proof as input and returns a boolean value indicating whether the proof is valid (i.e., the user's score falls within the claimed category). If the verification succeeds, the user's creditworthiness score within the system is updated accordingly.

Security Considerations and Scalability

The design of the reputation calculation engine prioritizes both security and scalability to ensure the system's robustness and efficiency in handling large user bases and transaction volumes.

• Security Considerations:

  • Secure Enclave Execution: Computationally intensive tasks like ZK-proof generation and verification can be performed within a secure enclave environment. This isolated execution environment protects the system's cryptographic keys and sensitive data from potential compromises.

  • Homomorphic Encryption: Sensitive financial data can be encrypted using homomorphic encryption techniques. This allows for calculations to be performed on encrypted data without decryption, further enhancing data privacy.

• Scalability Considerations:

  • Merkle Trees: Merkle trees can be employed to efficiently store and verify the integrity of historical ZK-proofs. This allows for efficient auditing and retrieval of past reputation scores while maintaining data integrity.

  • Off-chain Computation: Computationally intensive tasks associated with ZK-proofs can be outsourced to off-chain validator nodes in a decentralized manner. This approach distributes the processing load and improves the system's scalability.

By incorporating these security and scalability considerations, the reputation calculation engine fosters a secure and efficient environment for creditworthiness assessment within the Digital Dock ecosystem.

Conclusion

The meticulously designed architecture, encompassing the data aggregation layer and the reputation calculation engine, paves the way for a robust and trustworthy on-chain reputation system. This system leverages a combination of traditional financial data analysis and innovative blockchain techniques to provide a comprehensive evaluation of user creditworthiness. The utilization of ZK-STARK proofs empowers the system to maintain user privacy while ensuring the verifiability of reputation score updates. By prioritizing security and scalability, the system lays the groundwork for a future of decentralized finance built on trust and transparency.