PAC: Privacy-Preserving Arrhythmia Classification with Neural Networks - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

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.
Fichier principal
Vignette du fichier
publi-6046.pdf (1.13 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

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

Identifiers

Cite

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⟩
55 View
90 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More