Distributed Database Fraud Management

Distributed Database Fraud Management

Key Glossary

Blockchain: A distributed database technology that uses cryptography to link blocks of data together to form an immutable transaction record.

Smart Contract: A program stored on a blockchain that is automatically executed when pre-set conditions are met.

Hashing Algorithm: An algorithm that converts data of arbitrary length into a fixed-length string, commonly used for encryption and data integrity verification.

**SHA-2: ** A series of secure hash algorithms, including SHA-256, SHA-512, etc., commonly used to generate data summaries and digital signatures.

Public Key: One of a pair of keys in an asymmetric encryption algorithm that can be shared publicly and used to verify digital signatures and encrypt data.

Private Key: A key paired with a public key in an asymmetric encryption algorithm that must be kept strictly confidential and is used to generate digital signatures and decrypt data.

Blockchain Node: A computer connected to a blockchain network that maintains a complete or partial copy of blockchain data.

Consensus Mechanism: A mechanism by which nodes in a blockchain network reach consensus, used to verify transactions and add new data blocks to the blockchain.

Fraud Report: A report that records potential or confirmed fraudulent transactions, which can include transaction information, risk level, user reputation, etc.

Transaction Account Issuer: A financial institution that issues transaction accounts such as credit cards and debit cards.

Short Answer Question

How does the fraud management system described use blockchain technology?

How does the system generate and use payment request hash values?

What key information does the fraud report in the system contain?

What role does user reputation play in the system?

What special permissions does the transaction account issuer have in confirming fraud reports?

How is the participant registration process implemented in the system?

How do merchants use the system to query potential fraudulent transactions?

How do merchants report potential fraudulent transactions to the system?

What are the advantages of this system compared to traditional fraud management systems?

How does the system ensure data security and reliability?

Short Answer Question Answer

The system uses blockchain as a distributed database to store and manage fraud reports. The fraud management process is implemented through smart contracts, and hashing algorithms are used to protect data security.

The system uses a hash algorithm to convert the key information of the payment request into a hash value. The hash value is used to query and match potential fraudulent transactions on the blockchain.

The fraud report contains information such as the payment request hash value, the number of fraud reports, the user's reputation level and confidence.

The user's reputation level is based on factors such as the accuracy and activity of the user's reporting of fraudulent transactions. Reports from high-reputation users have higher confidence.

Fraud reports submitted by the issuer of the transaction account are considered confirmed fraud records with a confidence level of 100%.

Users submit registration requests through the participant registration portal, including identity information and public key addresses. The system verifies the identity information and writes the registration data to the blockchain.

The merchant sends the payment request to the system, the system generates a payment request hash value and queries the matching fraud report on the blockchain.

The merchant sends the potential fraud transaction information to the system, the system generates a hash value and writes it to the blockchain as a potential fraud report.

Compared with traditional systems, this system has the advantages of decentralization, high transparency, data immutability, and real-time updates.

The system ensures the security and reliability of data by using hash algorithms, asymmetric encryption, consensus mechanisms and other technologies