@inproceedings{866a5898bd564d9d8ea4e2358d87c706,
title = "ECONoMy: Ensemble collaborative learning using masking",
abstract = "In a society where digital data has become ubiquitous and has been projected to continue in this trajectory for the foreseeable future, machine learning has become a dependable tool to aid in analyzing these big datasets. However, where the data or machine learning algorithms are considered to be privacy-sensitive, one is then faced with the challenge of preserving the utility of machine learning in a privacy-preserving setting. In this paper, we focus on a use case where decentralized parties have privately owned machine learning algorithms, and would want to jointly generate a public model while not violating the privacy of their individual models, and data. We present ECONoMy: a privacy-preserving protocol that supports collaborative learning using an ensemble technique. Set in an honest-but-curious security model, ECONoMy is lightweight and provides efficiency and privacy in settings with large participant such as with IoT devices.",
keywords = "Collaborative, Ensemble, Machine Learning, Masking, Privacy-Preserving",
author = "{Van De Kamp}, Lars and Chibuike Ugwuoke and Zekeriya Erkin",
year = "2019",
month = sep,
day = "1",
doi = "10.1109/PIMRCW.2019.8880822",
language = "English",
series = "2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC Workshops 2019",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC Workshops 2019",
address = "United States",
note = "30th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC Workshops 2019 ; Conference date: 08-09-2019",
}