Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12258/15693
Title: A survey on privacy-preserving machine learning with fully homomorphic encryption
Authors: Babenko, M. G.
Бабенко, М. Г.
Keywords: Cloud security;Machine learning as a service;Cryptography;Fully homomorphic encryption
Issue Date: 2021
Publisher: Springer Science and Business Media Deutschland GmbH
Citation: Pulido-Gaytan, L.B., Tchernykh, A., Cortés-Mendoza, J.M., Babenko, M., Radchenko, G. A survey on privacy-preserving machine learning with fully homomorphic encryption // Communications in Computer and Information Science. - 2021. - Volume 1327. - Pages 115-129
Series/Report no.: Communications in Computer and Information Science
Abstract: The secure and efficient processing of private information in the cloud computing paradigm is still an open issue. New security threats arise with the increasing volume of data into cloud storage, where cloud providers require high levels of trust, and data breaches are significant problems. Encrypting the data with conventional schemes is considered the best option to avoid security problems. However, a decryption process is necessary when the data must be processed, but it falls into the initial problem of data vulnerability. The user cannot operate on the data directly and must download it to perform the computations locally. In this context, Fully Homomorphic Encryption (FHE) is considered the holy grail of cryptography in order to solve cybersecurity problems, it allows a non-trustworthy third-party resource to blindly process encrypted information without disclosing confidential data. FHE is a valuable capability in a world of distributed computation and heterogeneous networking. In this survey, we present a comprehensive review of theoretical concepts, state-of-the-art, limitations, potential applications, and development tools in the domain of FHE. Moreover, we show the intersection of FHE and machine learning from a theoretical and a practical point of view and identify potential research directions to enrich Machine Learning as a Service, a new paradigm of cloud computing. Specifically, this paper aims to be a guide to researchers and practitioners interested in learning, applying, and extending knowledge in FHE over machine learning
URI: http://hdl.handle.net/20.500.12258/15693
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

Files in This Item:
File SizeFormat 
scopusresults 1623 .pdf422.5 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.