Zobrazeno 1 - 10
of 159
pro vyhledávání: '"CKKS"'
Publikováno v:
Cybersecurity, Vol 7, Iss 1, Pp 1-21 (2024)
Abstract Unprotected gradient exchange in federated learning (FL) systems may lead to gradient leakage-related attacks. CKKS is a promising approximate homomorphic encryption scheme to protect gradients, owing to its unique capability of performing o
Externí odkaz:
https://doaj.org/article/841dd36c89f843ce9556212527a0d9af
Publikováno v:
Iraqi Journal for Computer Science and Mathematics, Vol 5, Iss 3 (2024)
Digitization of healthcare data has shown an urgent necessity to deal with privacy concerns within the field of deep learning for healthcare organizations. A promising approach is federated transfer learning, enabling medical institutions to train de
Externí odkaz:
https://doaj.org/article/68f6266be2524520b6946ad3b56a7a42
Publikováno v:
IEEE Access, Vol 12, Pp 110762-110780 (2024)
Although there has been significant progress in homomorphic encryption (HE) technology, a fully homomorphic Naive Bayes (NB) classifier capable of training on HE-encrypted data without decryption has not yet been efficiently developed. This research
Externí odkaz:
https://doaj.org/article/6b345d253bf7452996dc01860e4fefff
Autor:
Zeyu Wang, Makoto Ikeda
Publikováno v:
IEEE Access, Vol 12, Pp 94008-94017 (2024)
Homomorphic encryption (HE) is a promising method in privacy-preserving cloud computing. Applying HE on feedforwad neural networks has been frequently reported recently but the research on recurrent neural networks is still insufficient. In previous
Externí odkaz:
https://doaj.org/article/baa995a154484fc48bf2e8fa24c25562
Publikováno v:
IEEE Access, Vol 12, Pp 75965-75982 (2024)
Spectral clustering, a powerful algorithm in the field of AI, holds a significant role despite its inherent high time complexity. For data owners grappling with limitations such as small datasets and restricted computational resources, harnessing the
Externí odkaz:
https://doaj.org/article/365b64b7b8a044a489e7e33c66bbc855
Publikováno v:
Transactions on Cryptographic Hardware and Embedded Systems, Vol 2024, Iss 2 (2024)
Approximate arithmetic-based homomorphic encryption (HE) scheme CKKS [CKKS17] is arguably the most suitable one for real-world data-privacy applications due to its wider computation range than other HE schemes such as BGV [BGV14], FV and BFV [Bra12,
Externí odkaz:
https://doaj.org/article/7dce14f585f943a1b28817ae4cd70168
Publikováno v:
Sensors, Vol 24, Iss 15, p 4826 (2024)
Hierarchical clustering is a widely used data analysis technique. Typically, tools for this method operate on data in its original, readable form, raising privacy concerns when a clustering task involving sensitive data that must remain confidential
Externí odkaz:
https://doaj.org/article/b2b63ac1d0e847f1939ae4d79d8c48ed
Publikováno v:
Journal of Intelligent Systems, Vol 32, Iss 1, Pp 461-85 (2023)
Content-based image retrieval (CBIR) is a technique used to retrieve image from an image database. However, the CBIR process suffers from less accuracy to retrieve many images from an extensive image database and prove the privacy of images. The aim
Externí odkaz:
https://doaj.org/article/c665c312a37e4ec8b18c4070bfc8af85
Autor:
Hyunhoon Lee, Youngjoo Lee
Publikováno v:
IEEE Access, Vol 11, Pp 104775-104788 (2023)
Homomorphic encryption (HE) based on the CKKS scheme is a promising candidate for implementing privacy-preserving deep neural networks (PP-DNN) by performing operations directly on the encrypted data. However, due to the computational complexity of H
Externí odkaz:
https://doaj.org/article/ee3f9a9c78fe4c2883b4c1840dbef5d1
Publikováno v:
IEEE Access, Vol 11, Pp 62062-62076 (2023)
Homomorphic encryption (HE) is one of the representative solutions to privacy-preserving machine learning (PPML) classification enabling the server to classify private data of clients while guaranteeing privacy. This work focuses on PPML using word-w
Externí odkaz:
https://doaj.org/article/d5379254a4fc487198d0d77fb2183fd7