Research Article
Privacy-Preserving Property Prediction for New Drugs with MPNN
@INPROCEEDINGS{10.1007/978-3-030-89814-4_32, author={Jiaye Xue and Xinying Liao and Ximeng Liu and Wenzhong Guo}, title={Privacy-Preserving Property Prediction for New Drugs with MPNN}, proceedings={Mobile Multimedia Communications. 14th EAI International Conference, Mobimedia 2021, Virtual Event, July 23-25, 2021, Proceedings}, proceedings_a={MOBIMEDIA}, year={2021}, month={11}, keywords={Privacy-preserving Message passing neural network Drug discovery}, doi={10.1007/978-3-030-89814-4_32} }- Jiaye Xue
Xinying Liao
Ximeng Liu
Wenzhong Guo
Year: 2021
Privacy-Preserving Property Prediction for New Drugs with MPNN
MOBIMEDIA
Springer
DOI: 10.1007/978-3-030-89814-4_32
Abstract
Message passing neural network (MPNN) is one of the excellent deep learning models for drug discovery and development, drug laboratory usually outsource the MPNN model to cloud servers to save the research and development cost. However, drug-related data privacy has become a noticeable hindrance for outsourcing cooperation in drug discovery. In this paper, we propose a lightweight privacy-preserving message passing neural network framework (SecMPNN) for property prediction in new drugs. To implement SecMPNN, we design multiple protocols to perform the three stages of MPNN, namely message function, update function, and readout function. The above new-designed secure protocols enable SecMPNN to adapt to the different numbers of participating servers and different lengths of encryption requirements. Moreover, the accuracy, efficiency, and security of SecMPNN are demonstrated through comprehensive theoretical analysis and a large number of experiments. The experimental results show the communication efficiency in multiplication and comparison increases 27.78% and 58.75%, the computation error decreases to 4.64%.
