Challenges Of Using Homomorphic Encryption In Machine Learning

descriptionPublicationkeyboard_double_arrow_right Conference object , Other literature type , Article 01 Jan 2022 English Publisher:ZenodoJournal:18th International Conference on Artificial Intelligence Applications and Innovations (AIAI2022)Funded by:EC | LeADS

Authors: MESTARI, Soumia Zohra EL;

doi: 10.5281/zenodo.7491533 , 10.5281/zenodo.7491532

Challenges Of Using Homomorphic Encryption In Machine Learning

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Abstract

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In recent years, homomorphic encryption has been widely studied as a privacy enhancing technology to be applied in machine learning. Despite the strong security guarantees, this adoption faces many challenges that are discussed in this poster.

Related Organizations

University of Luxembourg
Luxembourg

Keywords

Privacy Preserving Machine Learning, Homomorphic encryption

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