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PAC: Privacy-Preserving Arrhythmia Classification with Neural Networks

Abstract : 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.
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Submitted on : Wednesday, February 24, 2021 - 2:28:25 PM
Last modification on : Saturday, May 8, 2021 - 3:38:43 AM
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Mohamad Mansouri, Beyza Bozdemir, Melek Önen, Orhan Ermis. PAC: Privacy-Preserving Arrhythmia Classification with Neural Networks. FPS 2019, International Symposium on Foundations and Practice of Security, Nov 2019, Toulouse, France. pp.3-19, ⟨10.1007/978-3-030-45371-8_1⟩. ⟨hal-03151095⟩



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