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.
PAC: Privacy-preserving Arrhythmia Classification with neural networks
FPS 2019, 12th International Symposium on Foundations and Practice of Security, November 5-7, 2019, Toulouse, France / Also published in LNCS, Vol. 12056
Best Paper Award
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in FPS 2019, 12th International Symposium on Foundations and Practice of Security, November 5-7, 2019, Toulouse, France / Also published in LNCS, Vol. 12056 and is available at : https://doi.org/10.1007/978-3-030-45371-8_1
PERMALINK : https://www.eurecom.fr/publication/6046