PAC: Privacy-preserving Arrhythmia Classification with neural networks

In this paper, we propose to study privacy concerns raised by the analysis of Electro CardioGram (ECG) data for arrhythmia classification. We propose a solution named PAC that combines the use of Neural Networks (NN) with secure two-party computation in order to enable an efficient NN prediction of arrhythmia without discovering the actual ECG data. To achieve a good trade-off between privacy, accuracy, and efficiency, we first build a dedicated NN model which consists of two fully connected layers and one activation layer as a square function. The solution is implemented with the ABY framework. PAC also supports classifications in batches. Experimental results show an accuracy of 96.34% which outperforms existing solutions.