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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Notes
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Lectures 1&2: Introduction to Secure Computation, Yao’s and GMW Protocols, Secure Computation Course at Berkeley University.
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Acknowledgments
This work was partly supported by the PAPAYA project funded by the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement no. 786767.
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Mansouri, M., Bozdemir, B., Önen, M., Ermis, O. (2020). PAC: Privacy-Preserving Arrhythmia Classification with Neural Networks. In: Benzekri, A., Barbeau, M., Gong, G., Laborde, R., Garcia-Alfaro, J. (eds) Foundations and Practice of Security. FPS 2019. Lecture Notes in Computer Science(), vol 12056. Springer, Cham. https://doi.org/10.1007/978-3-030-45371-8_1
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