Privacy-Preserving Neural Networks with Decentralized Multi-client Functional Encryption

Emerging machine learning methods have become a powerful driving force to revolutionize many industries nowadays, such as banking, healthcare services, retail, manufacturing, transportation. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. Functional encryption is a new type of encryption primitive in which a secret functional-key allows one to compute a specific function of plaintext from the ciphertext, making it very suitable for privacy protection machine learning scenarios. In this paper, we apply the concepts of decentralized multi-client function encryption to explore a new solution to the privacy-preserving convolutional neural network. The results of the experiment show that our scheme is feasible, and the accuracy of the final model on the test set is 92.1%, which is close to 93.2% of the convolution network connected to plain text.