4.5 Consensus and Security
Every verification result produced by each Verifier will be submitted to other Verifier nodes for repeated computations to confirm the credibility and consistency of the verification.
Possible attacks:
51% Attack: Attackers acquire more than 51% of a network’s mining hashrate, allowing them to control the entire blockchain network. This enables the attacker to prevent transactions or modify block records.
Malicious Credit Data Injection: Attackers may attempt to inject false or malicious credit data into the blockchain to disrupt the system or deceive other users.
Consensus Attack: Attack the verify nodes in order to alter the verify process and voting, resulting in the creation of false Checker.
Methods to prevent attacks:
51% Attack
Ensure that the committee has a significant amount of token holdings, making it costly for attackers to acquire a large number of tokens.
Increase the number of verify nodes participating in the transaction.
By implementing the above measures, the cost for attackers would be increased to an unacceptable level.
Malicious Credit Data Injection
Data Validation and Cleaning: Before accepting any credit data, perform rigorous data validation and cleaning. Ensure that the data format is correct, conforms to the expected structure, and complies with the predefined data range. Filter out illegal or abnormal data.
Identity Verification: Implement optional identity verification mechanisms to ensure that the data sources are legitimate and trusted entities. Utilize multi-factor authentication, biometric recognition, or other advanced identity verification technologies to ensure that the source of the data is trustworthy.
Encryption and Digital Signatures: Utilize robust encryption algorithms for transmitting and storing credit data. Digital signatures can be used to verify the integrity and origin of data. Only data with valid digital signatures should be accepted as trusted data.
De-identification and Differential Privacy: Implement de-identification techniques during the collection and processing of credit data to prevent potential leaks of sensitive information. Differential privacy techniques can provide aggregated statistics without exposing specific individual information, thereby enhancing user privacy protection.
Blockchain storage: Store credit data on the blockchain to ensure the immutability and transparency of data. Data on the blockchain is distributed, and it is difficult to tamper with once written.
Artificial Intelligence and Machine Learning: Utilize machine learning algorithms to detect abnormal patterns in order to identify false credit data promptly. This includes detecting abnormal credit history, behavioral patterns, or other credit-related features.
Real-time Monitoring and Alert System: Deploy a real-time monitoring system that can detect abnormal behavior and issue alerts promptly. This helps to quickly respond to potential attacks.
Regulatory Compliance: Ensure that the system design is in compliance with relevant laws and regulatory requirements. Compliance frameworks typically include provisions for data privacy and protection, the breach of which may result in legal liability.
User participation and authorization: Enable users to access and control their credit data, ensuring that they consent to the purpose of data usage. Transparency and user participation are key in building trust.
Consensus Attack
Distributed Nodes: Distribute nodes across different geographical locations, operated by different organizations or individuals. This makes it more challenging for attackers as they would need to compromise multiple nodes simultaneously to mount an attack.
Strengthening Identity Verification: Only nodes with sufficient stake or other verifiable identity information can participate in the consensus process.
Punishment Mechanism: Implement a punishment mechanism for malicious behavior to reduce the motivation for attackers. For non-compliant nodes that do not follow to consensus rules, reduce their stake or other rewards.
Real-time Monitoring: Deploy a real-time monitoring system that can detect abnormal behavior and take appropriate measures. This includes detecting unusual network activities, transaction anomalies, and other suspicious behavior.
Regulatory Compliance: Ensure that the system design complies with relevant laws and regulatory requirements.
Decentralization: Embrace the principle of decentralization, avoiding excessive reliance on any single node or entity. This ensures that even if certain nodes are attacked, the entire system can continue to operate resiliently.
Network Segregation: Segregate nodes with different consensus mechanisms into separate networks to prevent an attack on one consensus mechanism from affecting the others.
Last updated