Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12258/15562
Title: Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities
Authors: Babenko, M. G.
Бабенко, М. Г.
Keywords: Cloud security;Homomorphic encryption;Machine learning;Neural networks;Privacy-preserving
Issue Date: 2021
Publisher: Springer
Citation: Pulido-Gaytan, B., Tchernykh, A., Cortés-Mendoza, J.M., Babenko, M., Radchenko, G., Avetisyan, A., Drozdov, A.Y. Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities // Peer-to-Peer Networking and Applications. - 2021
Series/Report no.: Peer-to-Peer Networking and Applications
Abstract: Classical machine learning modeling demands considerable computing power for internal calculations and training with big data in a reasonable amount of time. In recent years, clouds provide services to facilitate this process, but it introduces new security threats of data breaches. Modern encryption techniques ensure security and are considered as the best option to protect stored data and data in transit from an unauthorized third-party. However, a decryption process is necessary when the data must be processed or analyzed, falling into the initial problem of data vulnerability. Fully Homomorphic Encryption (FHE) is considered the holy grail of cryptography. It allows a non-trustworthy third-party resource to process encrypted information without disclosing confidential data. In this paper, we analyze the fundamental concepts of FHE, practical implementations, state-of-the-art approaches, limitations, advantages, disadvantages, potential applications, and development tools focusing on neural networks. In recent years, FHE development demonstrates remarkable progress. However, current literature in the homomorphic neural networks is almost exclusively addressed by practitioners looking for suitable implementations. It still lacks comprehensive and more thorough reviews. We focus on the privacy-preserving homomorphic encryption cryptosystems targeted at neural networks identifying current solutions, open issues, challenges, opportunities, and potential research directions
URI: http://hdl.handle.net/20.500.12258/15562
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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