Security and Compliance

dMarketplace prioritizes the security and compliance of its platform to safeguard users and maintain regulatory adherence.

a) Data Encryption and Security Protocols

Encryption Standards: All user data is encrypted using AES-256 and SHA-256 hashing algorithms, ensuring robust protection against unauthorized access and data breaches.
Decentralized Identity Verification: Utilizing zero-knowledge proofs (zk-SNARKs), dMarketplace ensures user anonymity while maintaining the integrity of KYB and KYC processes.
Secure Key Management: Encryption keys are securely managed and stored on-chain, employing multi-signature wallets and hardware security modules (HSMs) to prevent key compromise.

Real-Time Monitoring: The AI system continuously monitors transaction patterns and user behaviors to detect anomalies indicative of fraudulent activities.
Pattern Recognition: Machine learning models analyze historical data to identify common fraud indicators, enabling proactive prevention measures.
Automated Alerts and Actions: Upon detecting suspicious activities, the AI system automatically flags the account, initiates a review, and can trigger smart contract actions such as freezing funds or issuing penalties.
Adaptive Learning: The AI models are designed to learn and evolve, improving their accuracy and effectiveness in fraud detection over time.

Last updated 1 month ago

dMarketplace prioritizes the security and compliance of its platform to safeguard users and maintain regulatory adherence.

Encryption Standards: All user data is encrypted using AES-256 and SHA-256 hashing algorithms, ensuring robust protection against unauthorized access and data breaches.
Decentralized Identity Verification: Utilizing zero-knowledge proofs (zk-SNARKs), dMarketplace ensures user anonymity while maintaining the integrity of KYB and KYC processes.
Secure Key Management: Encryption keys are securely managed and stored on-chain, employing multi-signature wallets and hardware security modules (HSMs) to prevent key compromise.

Real-Time Monitoring: The AI system continuously monitors transaction patterns and user behaviors to detect anomalies indicative of fraudulent activities.
Pattern Recognition: Machine learning models analyze historical data to identify common fraud indicators, enabling proactive prevention measures.
Automated Alerts and Actions: Upon detecting suspicious activities, the AI system automatically flags the account, initiates a review, and can trigger smart contract actions such as freezing funds or issuing penalties.
Adaptive Learning: The AI models are designed to learn and evolve, improving their accuracy and effectiveness in fraud detection over time.

Last updated 1 month ago