计算机科学
加密
计算机安全
人工神经网络
计算机网络
互联网隐私
人工智能
作者
Changji Wang,Xinyu Zhou,Panpan Li,Ning Liu
出处
期刊:Communications in computer and information science
日期:2022-01-01
卷期号:: 656-667
标识
DOI:10.1007/978-3-031-06761-7_52
摘要
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.
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