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Communication Dans Un Congrès Année : 2020

PAC: Privacy-Preserving Arrhythmia Classification with Neural Networks

Mohamad Mansouri
Melek Önen

Résumé

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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Dates et versions

hal-03151095 , version 1 (24-02-2021)

Identifiants

Citer

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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