Abstract: Deep neural network (DNN) is powerful and widely used for gene testing tasks in biomedi- cal fields. Building a reliable DNN model requires a large number of valid medical samples, while in reality, biological data with high privacy are usually stored in a decentralized manner. Existing solutions struggle to achieve both data security and high model accuracy when dealing with such distributed and large-scale complex learning tasks. To mitigate this problem, a novel privacy-preserving scheme based on the DNN model is proposed, which combines multiple data sources and quickly constructs a high- precision gene testing model. Firstly, the mask matrix is combined with the functional encryption for inner product to eliminate the approximate substitution strategies required in schemes such as fully homomorphic and secret sharing, thereby achieving consistency between the privacy-preserving and the centralized DNN training. Secondly, a non-interactive DNN training mode is constructed to resist the inference attacks caused by global model parameters leakage, ensuring the security of data. Experimental results on real medical datasets demonstrate the correctness, effectiveness, and high accuracy of the proposed scheme. 

Key words: deep neural network, gene testing, privacy-preserving, mask matrix, functional encryption for inner product