Results 311 to 320 of about 1,487,229 (359)

An Ethical Approach to Genomic Privacy Preserving Technology Development. [PDF]

Proc (IEEE Int Conf Healthc Inform)
Gerido LH, Ayday E.
europepmc   +1 more source

Data-driven and privacy-preserving risk assessment method based on federated learning for smart grids. [PDF]

Commun Eng
Deng S, Zhang L, Yue D.
europepmc   +1 more source

Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine. [PDF]

Transfusion
Li N, Lewin A, Ning S, Waito M, Zeller MP, Tinmouth A, Shih AW, Canadian Transfusion Trials Group.   +7 more
europepmc   +1 more source

Use Cases Requiring Privacy-Preserving Record Linkage in Paediatric Oncology. [PDF]

Cancers (Basel)
Hayn D, Kreiner K, Sandner E, Baumgartner M, Jammerbund B, Falgenhauer M, Düster V, Devi-Marulkar P, Schleiermacher G, Ladenstein R, Schreier G.   +10 more
europepmc   +1 more source

A New Era of Text Mining in Radiology with Privacy-Preserving LLMs. [PDF]

Radiol Artif Intell
Akinci D'Antonoli T, Bluethgen C.
europepmc   +1 more source

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EPPDA: An Efficient Privacy-Preserving Data Aggregation Federated Learning Scheme

IEEE Transactions on Network Science and Engineering, 2023
Federated learning (FL) is a kind of privacy-awaremachine learning, in which the machine learning models are trained on the users’ side and then the model updates are transmitted to the server for aggregating.
Jingcheng Song, Weizheng Wang, T. Gadekallu, Jianyu Cao, Yining Liu   +4 more
semanticscholar   +1 more source

Privacy-Preserving Byzantine-Robust Federated Learning via Blockchain Systems

IEEE Transactions on Information Forensics and Security, 2022
Federated learning enables clients to train a machine learning model jointly without sharing their local data. However, due to the centrality of federated learning framework and the untrustworthiness of clients, traditional federated learning solutions ...
Yinbin Miao, Ziteng Liu, Hongwei Li, K. Choo, R. Deng   +4 more
semanticscholar   +1 more source

ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning

IEEE Transactions on Information Forensics and Security, 2022
Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning paradigm that aggregates user-trained local gradients into a federated model through a cryptographic protocol.
Zhuo Ma, Jianfeng Ma, Yinbin Miao, Yingjiu Li, R. Deng   +4 more
semanticscholar   +1 more source

PMRSS: Privacy-Preserving Medical Record Searching Scheme for Intelligent Diagnosis in IoT Healthcare

IEEE Transactions on Industrial Informatics, 2022
In medical field, previous patients’ cases are extremely private as well as intensely valuable to current disease diagnosis. Therefore, how to make full use of precious cases while not leaking out patients’ privacy is a leading and promising work ...
Yi Sun, Jie Liu, K. Yu, M. Alazab, Kaixiang Lin   +4 more
semanticscholar   +1 more source