Spatial federated learning and blockchain-based 5G communication model for hiding confidential information


At present, the preferred method of transmitting a rapid blockchain message is to send several transactions, constituting a covert 5G communication technique. However, this approach is inadequate for processing larger quantities of sensitive data, and the potential for losing confidential information is significant. Additionally, the sender’s identity is not concealed. Despite the high embedding rate of steganography techniques, they are increasingly vulnerable to detection and statistical feature-based analysis. This investigation suggests a covert blockchain communication methodology that incorporates spatial federated learning and spatial blockchain as a means of fixing these issues. By utilizing Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to encrypt the sensitive document and uploading it to the Inter Planetary File System (IPFS), the technique conceals sensitive files and the sender’s identity. Then, using image steganography based on Generative Adversarial Networks (GAN), the sender implants the hash value of the encrypted document into a carrier image. After uploading the encrypted image to IPFS, the sender creates a transaction with the hash value of the encrypted image. This transaction is then signed by a ring signature and broadcasted to the blockchain network for verification and confirmation. The recipient retrieves the encrypted document and decrypts it according to the access control policy established by CP-ABE. According to experimental findings, this model can increase the volume of sensitive data transmitted from KB to MB while providing higher confidentiality and security.

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