A Blockchain-Based Fairness Guarantee Approach for Privacy-Preserving Collaborative Training in Computing Force Network

Abstract

The advent of the big data era has brought unprecedented data demands. The integration of computing resources with network resources in the computing force network enables the possibility of distributed collaborative training. However, unencrypted collaborative training is vulnerable to threats such as gradient inversion attacks and model theft. To address this issue, the data in collaborative training are usually protected by cryptographic methods. However, the semantic meaninglessness of encrypted data makes it difficult to prevent potential data poisoning attacks and free-riding attacks. In this paper, we propose a fairness guarantee approach for privacy-preserving collaborative training, employing blockchain technology to enable participants to share data and exclude potential violators from normal users. We utilize a cryptography-based secure aggregation method to prevent data leakage during blockchain transactions, and employ a contribution evaluation method for encrypted data to prevent data poisoning and free-riding attacks. Additionally, utilizing Shamir’s secret sharing for secret key negotiation within the group, the negotiated key is directly introduced as noise into the model, ensuring the encryption process is computationally lightweight. Decryption is efficiently achieved through the aggregation of encrypted models within the group, without incurring additional computational costs, thereby enhancing the computational efficiency of the encryption and decryption processes. Finally, the experimental results demonstrate the effectiveness and efficiency of our proposed approach.

A Data Attack Detection Framework for Cryptography-Based Secure Aggregation Methods in 6G Intelligent Applications

“…This section will compare various encryption methods and discuss the 6G scenarios suitable for each method, as shown in Table 10. When the size of the encryption group increases, the encryption time for Paillier increases linearly, but the encryption time for BCP and the method from the literature [42] remains constant. The method from the literature's [42] encryption takes less time.…”

“…When the size of the encryption group increases, the encryption time for Paillier increases linearly, but the encryption time for BCP and the method from the literature [42] remains constant. The method from the literature's [42] encryption takes less time. When the message length is 300, the encryption times for BCP and Paillier are almost the same.…”

“…When the message length is 300, the encryption times for BCP and Paillier are almost the same. Thus, choosing an encrypted message length of 300 does not result in higher encryption time consumption for either BCP or the method from the literature [42].…”

“…The decryption time consumptions for BCP master key encryption and private key encryption were compared with the Paillier encryption algorithm. Since the decryption process of the method from the literature [42] encryption algorithm model is consistent with the normal model aggregation process, we have omitted the decryption process, as shown in Table 12. When the encrypted message length increases, the decryption time for each ciphertext remains unchanged.…”

“…The method from the literature [42] does not require complex iterative processes or large-number computations; even with an increase in shares, its computational complexity remains relatively low, making the entire encryption and decryption process more efficient. For applications that require rapid data responses, it may be necessary to consider using the method from the literature [42].…”