WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality Technology provider, announced that the blockchain data encryption based on machine learning and fully homomorphic encryption algorithm is a comprehensive solution which applies cutting-edge cryptography and artificial intelligence technologies for blockchain data protection. It combines the intelligent key management of machine learning and the direct ciphertext computation capability of fully homomorphic encryption, aiming to ensure that the data on the blockchain achieves effective protection of sensitive information while maintaining a high degree of transparency and tamper-proofness.

Full homomorphic encryption (FHE), as an advanced cryptographic technique, can perform arithmetic operations on encrypted data without first decrypting it, the result of the computation remains encrypted, and the decrypted result is the same as the result of the direct computation on the plaintext. This technology provides new ideas for solving blockchain privacy issues. FHE can also support more complex operations, such as exponentiation, division, comparison, etc., making it possible to execute machine learning models on encrypted data. By fully homomorphic encryption of sensitive data on the blockchain (e.g., transaction amounts, user identities, smart contract parameters, etc.), it ensures that while this information is open and transparent on the chain, only the data owner or authorized participants can decrypt and access the specific content, realizing the harmonious coexistence of privacy protection and the principle of blockchain transparency. After receiving the encrypted data, blockchain nodes can directly perform operations such as verification, bookkeeping, and smart contract execution on the ciphertext. FHE ensures that these operations do not expose plaintext information and the computation results remain encrypted. The data owner or the participant with the corresponding rights uses the private key to decrypt the encrypted results to make decisions, transfer assets, and confirm the results of contract execution. Unauthorized users cannot decrypt thus protecting data privacy.

The application of machine learning technology in information security is also expanding, especially in key management, threat detection, risk assessment, etc. With algorithm optimization and hardware acceleration, machine learning models are able to efficiently process large amounts of data in a real-time environment, analyze multi-dimensional information such as the network environment, user behavior, blockchain transaction patterns, etc. in real time, dynamically generate and update encryption keys, improve the randomness and anti-cracking ability of the keys, and realize the intelligent management of encryption systems. Using the dynamic key generated by machine learning to encrypt sensitive data on the blockchain can ensure the security of the data when it is broadcast and stored on the chain. At the same time, machine learning can also carry out a risk assessment and early warning of the blockchain system, adjust the encryption strategy according to the risk posture, enhance the adaptability and defense capability of data encryption, cope with changing means of attack and security threats, and ensure data security.

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WiMi‘s data encryption technology based on machine learning and fully homomorphic encryption algorithm can be utilized in blockchain in scenarios including privacy-protected transactions, private smart contracts, cross-chain data exchange and collaboration, on-chain data analysis and machine learning. For example, both parties to a transaction can use FHE to encrypt sensitive information such as transaction amount, asset type, and purpose of the transaction, ensuring that while this information is open and transparent on the chain, only both parties to the transaction and the necessary validation nodes can decrypt it and view it, thus protecting the privacy of the transaction. The code logic and input data of smart contracts can go through FHE first and then be executed on the chain. Even if the contract code and input data are visible to the public, the calculation results remain encrypted and only the contract participants can decrypt the results, protecting commercial secrets and the privacy of the execution process. In multi-chain or cross-chain environments, FHE ensures that data passed between different blockchains always remains encrypted, and only the authorized nodes of the destination chain can decrypt the data, preventing data leakage in the intermediate links and supporting secure cross-chain data sharing and collaboration. In addition, the encrypted data on the blockchain can be aggregated and statistically operated to generate encrypted analysis results, which facilitates on-chain or off-chain market trend analysis, risk assessment, etc., without exposing individual data. In addition, a decentralized machine learning platform based on homomorphic encryption can be established, where each participating node contributes encrypted training data to jointly train models and protect data privacy.

Blockchain data encryption technology based on machine learning and a fully homomorphic encryption algorithm integrates cutting-edge encryption and artificial intelligence technologies, aiming to provide strong data protection capabilities for blockchain while maintaining the transparency and decentralized characteristics of blockchain, which provides security for the blockchain ecosystem through intelligent key management, fully homomorphic encryption computation, and a balance between privacy protection and transparency. It meets the needs of increasingly complex application scenarios and promotes the development of blockchain technology towards a more secure, private, and practical way.

SOURCE: PRNewsWire