Graduate School and Research Center in Digital Sciences

PAC: Privacy-preserving Arrhythmia Classification with neural networks

Mansouri, Mohamad; Bozdemir, Beyza; Önen, Melek; Ermis, Orhan

FPS 2019, 12th International Symposium on Foundations and Practice of Security, November 5-7, 2019, Toulouse, France

Best Paper Award

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.

Document Bibtex

Title:PAC: Privacy-preserving Arrhythmia Classification with neural networks
Keywords:privacy · neural networks · arrhythmia classification
Department:Digital Security
Eurecom ref:6046
Bibtex: @inproceedings{EURECOM+6046, year = {2019}, title = {{PAC}: {P}rivacy-preserving {A}rrhythmia {C}lassification with neural networks}, author = {{M}ansouri, {M}ohamad and {B}ozdemir, {B}eyza and {\"{O}}nen, {M}elek and {E}rmis, {O}rhan}, booktitle = {{FPS} 2019, 12th {I}nternational {S}ymposium on {F}oundations and {P}ractice of {S}ecurity, {N}ovember 5-7, 2019, {T}oulouse, {F}rance}, address = {{T}oulouse, {FRANCE}}, month = {11}, url = {} }
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