Privacy-Preserving ECG Classification With Branching Programs and Neural Networks
By Mauro Barni, Pierluigi Failla, Riccardo Lazzeretti, Ahmad-Reza Sadeghi, Thomas Schneider. 2011
199 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yang Zhang
- Michael Backes
- Reza Shokri
- Farinaz Koushanfar
- Ahmed Salem
- Vitaly Shmatikov
- Mario Fritz
- M. Sadegh Riazi
In Computer Vision and Pattern Recognition
- Vitaly Shmatikov
- Yang Zhang
- Mario Fritz
- Michael Backes
- Reza Shokri
- Ahmed Salem
- Raphael Bost
- M. Sadegh Riazi
Citations in the corpus
- A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, background): “Though some efforts tried to apply encryption algorithms [115-118] to achieve privacy preservation in extracting features from images, the timeliness requirement in smart transportation makes it inappropriate to apply encryption and seriously obfuscates this issue.”
- Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, background): “Privacy-preserving classification of electrocardiogram (ECG) signals using neural networks has been addressed in [10].”
- Privacy-preserving deep learning (2015, background): “Techniques that protect privacy of the model include privacypreserving probabilistic inference [38], privacy-preserving speaker identification [36], and computing on encrypted data [3, 6, 55].”
- ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models (2018, background): “Another relevant line of work is privacy-preserving machine learning [6], [9]–[11], [13], [14], [17], [18], [25], [29].”
- XONN: XNOR-based Oblivious Deep Neural Network Inference (2019, methodology): “A solution based on additively homomorphic encryption and garbled circuits is presented in [41].”
- Machine Learning Classification over Encrypted Data (2015, background): “In a subsequent work [29], Barni et al.”
- A Review on the State-of-the-Art Privacy-Preserving Approaches in the e-Health Clouds (2014, unknown): “In [57], the authors introduced a multiparty”