@inproceedings{4ca31410b28a47a2938df83df5aadb4f,
title = "LED: Learnable Encryption with Deniability",
abstract = "User privacy is an important issue in the cloud machine learning service. In this paper, we raise a new threat about the online machine learning service, which comes from outside superior authority. The authority may ask the user and the cloud to disclose secrets and the authority can monitor the user behavior. We propose a protection approach called learnable encryption with deniability (LED), which can convince the outsider of the fake data and can protect the user privacy.",
keywords = "Deniable encryption, Learnable encryption, Privacy-preserving machine learning",
author = "Lin, {Zhe Wei} and Liu, {Tzu Hung} and Chi, {Po Wen}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022 ; Conference date: 15-12-2022 Through 17-12-2022",
year = "2022",
doi = "10.1007/978-981-19-9582-8_57",
language = "English",
isbn = "9789811995811",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "649--660",
editor = "Sun-Yuan Hsieh and Ling-Ju Hung and Sheng-Lung Peng and Ralf Klasing and Chia-Wei Lee",
booktitle = "New Trends in Computer Technologies and Applications - 25th International Computer Symposium, ICS 2022, Proceedings",
}