Zobrazeno 1 - 10
of 480
pro vyhledávání: '"privacy-preserving machine learning"'
Autor:
A. Anil Sinaci, Mert Gencturk, Celia Alvarez-Romero, Gokce Banu Laleci Erturkmen, Alicia Martinez-Garcia, María José Escalona-Cuaresma, Carlos Luis Parra-Calderon
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 24, Iss , Pp 136-145 (2024)
Objective: This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorit
Externí odkaz:
https://doaj.org/article/c4b3e00abe854c439527de20564ff355
Publikováno v:
Frontiers in Computer Science, Vol 6 (2024)
Federated learning (FL) has emerged as a promising paradigm for secure distributed machine learning model training across multiple clients or devices, enabling model training without having to share data across the clients. However, recent studies re
Externí odkaz:
https://doaj.org/article/1a11a6ca9ed9439f8f7e1f72397ac71a
Autor:
Jose L. Salmeron, Irina Arévalo
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-31 (2024)
Abstract Federated learning is an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. This method is secure and privacy-preserving, suitable for training a machine l
Externí odkaz:
https://doaj.org/article/95f4f1681ac84d4f80fb933b1403d2a3
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Deep learning has proven to be highly effective in diagnosing COVID-19; however, its efficacy is contingent upon the availability of extensive data for model training. The data sharing among hospitals, which is crucial for training robust mo
Externí odkaz:
https://doaj.org/article/f5b4179ad1ec4dc69079be6582c629d1
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:
Hyunmin Choi, Jihun Kim, Seungho Kim, Seonhye Park, Jeongyong Park, Wonbin Choi, Hyoungshick Kim
Publikováno v:
IEEE Access, Vol 12, Pp 109323-109341 (2024)
Homomorphic encryption (HE) enables privacy-preserving deep learning by allowing computations on encrypted data without decryption. However, deploying convolutional neural networks (CNNs) with HE is challenging due to the need to convert input data i
Externí odkaz:
https://doaj.org/article/565a9606175845359d71d39b55299898
Publikováno v:
IEEE Access, Vol 12, Pp 57043-57058 (2024)
Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. This approach raises several challenges due to the different statistica
Externí odkaz:
https://doaj.org/article/1be9762d70e546808673fc283b5ee9c5
Publikováno v:
IEEE Access, Vol 12, Pp 3024-3038 (2024)
Convolutional neural network (CNN) inference using fully homomorphic encryption (FHE) is a promising private inference (PI) solution due to the capability of FHE that enables offloading the whole computation process to the server while protecting the
Externí odkaz:
https://doaj.org/article/d0fcd4d2fa4b45098f04a02b13e8ca7c
Publikováno v:
Applied Sciences, Vol 14, Iss 21, p 9876 (2024)
Due to the expansion of Artificial Intelligence (AI), especially Machine Learning (ML), it is more common to face confidentiality regulations about using sensitive data in learning models generally hosted in cloud environments. Confidentiality regula
Externí odkaz:
https://doaj.org/article/091af61ccdea48bf9c1fe7e33c3a1e7e
Publikováno v:
Transactions on Cryptographic Hardware and Embedded Systems, Vol 2024, Iss 2 (2024)
Secure multi-party computation and homomorphic encryption are two primary security primitives in privacy-preserving machine learning, whose wide adoption is, nevertheless, constrained by the computation and network communication overheads. This paper
Externí odkaz:
https://doaj.org/article/1a0d06d407194293903d7dae54231b8c