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Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption [article]

Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite
2021 arXiv   pre-print
In this work, we propose a framework for secure FL using fully-homomorphic encryption (FHE).  ...  , and demonstrate that there is no degradation in the learning performance between the encrypted and non-encrypted federated models.  ...  Neuroimaging Analysis The focus of our work is on the problem of predicting subjects' brain age from 3D MRI scans in an encrypted federated learning environment.  ... 
arXiv:2108.03437v2 fatcat:5owpppcdrncq3a5xqoxb34nioq

Scaling Neuroscience Research using Federated Learning [article]

Dimitris Stripelis, Jose Luis Ambite, Pradeep Lam, Paul Thompson
2021 arXiv   pre-print
Federated Learning is a promising approach to learn a joint model over data silos.  ...  Here, we describe our Federated Learning architecture and training policies.  ...  Analysis of these vast datasets using machine learning approaches promises novel discoveries.  ... 
arXiv:2102.08440v1 fatcat:o2it275lerewxkoxxceud4ea7y

Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy [article]

Chandra Thapa, Seyit Camtepe
2020 arXiv   pre-print
Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects.  ...  It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers).  ...  approach, encryption [31] Federated learning Current progress on federated learning (technical) attacks and defenses Federated learning, secure multi-party computation, differential privacy [32] Human  ... 
arXiv:2008.10733v1 fatcat:oj2neoftf5hcbpatnfn7ntyhzy

Federated Learning: Issues in Medical Application [article]

Joo Hun Yoo, Hyejun Jeong, Jaehyeok Lee, Tai-Myoung Chung
2021 arXiv   pre-print
In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed.  ...  They are related to data/system heterogeneity, client management, traceability, and security.  ...  Hao combined differential privacy and additive homomorphic encryption to obtain both performance and security [21] .  ... 
arXiv:2109.00202v1 fatcat:qpavla4vafa6jpzuqfytejm5mq

COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets

Adam Scott, Will Courtney, Dylan Wood, Raul de la Garza, Susan Lane, Margaret King, Runtang Wang, Jody Roberts, Jessica A. Turner, Vince D. Calhoun
2011 Frontiers in Neuroinformatics  
intuitive ease of use and PHI security are emphasized as important attributes.  ...  The availability of well-characterized neuroimaging data with large numbers of subjects, especially for clinical populations, is critical to advancing our understanding of the healthy and diseased brain  ...  Finally, security and federal regulations emphasize tracking of encryption for data whose confidentiality is sensitive.  ... 
doi:10.3389/fninf.2011.00033 pmid:22275896 pmcid:PMC3250631 fatcat:gj3pzuuserasvbyipsdr4unwme

Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health [article]

Guodong Long, Tao Shen, Yue Tan, Leah Gerrard, Allison Clarke, Jing Jiang
2021 arXiv   pre-print
This chapter will discuss how federated learning can enable the development of an open health ecosystem with the support of AI.  ...  Federated learning is a new machine learning paradigm to learn a shared model across users or organisations without direct access to the data.  ...  Moreover, novel privacy preserving techniques, i.e. homomorphic encryption (HE) and secret sharing can be incorporated with learning models, i.e. neural networks, under the proposed FTL framework without  ... 
arXiv:2108.10761v1 fatcat:lp4wuhb4sre7toacoba6upfcxq

Federated Learning for Smart Healthcare: A Survey [article]

Dinh C. Nguyen, Quoc-Viet Pham, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia A. Dobre, Won-Joo Hwang
2021 arXiv   pre-print
Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training  ...  Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare.  ...  According to [57] , secure aggregation approaches can be classified into secure communication protocols, dining cryptographers (DC) based secure aggregation, homomorphic threshold encryption, and pairwise  ... 
arXiv:2111.08834v1 fatcat:jmex4e25rbgy3bk67iolrj4uee

Cloud-based genomics pipelines for ophthalmology: reviewed from research to clinical practice

David C.S. Wong, Maximiliano Olivera, Jing Yu, Anita Szabo, Ismail Moghul, Konstantinos Balaskas, Robert Luben, Anthony P. Khawaja, Nikolas Pontikos, Pearse A. Keane
2021 Modeling and Artificial Intelligence in Ophthalmology  
Aim: To familiarize clinicians with clinical genomics, and to describe the potential of cloud computing for enabling the future routine use of genomics in eye hospital settings.Design: Review article exploring  ...  routinely used in clinical practice.  ...  Authorized research groups are able to access the genetic data via the cloud and use homomorphic encryption to analyze the data securely, whilst discovering new insights about the pathogenesis of glaucoma  ... 
doi:10.35119/maio.v3i1.115 fatcat:wjxxoar6kfhufmrkw6tj53spna

Improved Differentially Private Decentralized Source Separation for fMRI Data [article]

Hafiz Imtiaz, Jafar Mohammadi, Rogers Silva, Bradley Baker, Sergey M. Plis, Anand D. Sarwate, Vince Calhoun
2021 arXiv   pre-print
Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data.  ...  In order to leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations.  ...  Other approaches to using DP in federated learning operate in different regimes, such as learning from a large number of individual data holders, or learning from silos with a large number of data points  ... 
arXiv:1910.12913v2 fatcat:goebuhpzn5gvjohezl6dzmapme

Towards a Governance Framework for Brain Data [article]

Marcello Ienca, Joseph J. Fins, Ralf J. Jox, Fabrice Jotterand, Silja Voeneky, Roberto Andorno, Tonio Ball, Claude Castelluccia, Ricardo Chavarriaga, Hervé Chneiweiss, Agata Ferretti, Orsolya Friedrich (+8 others)
2021 arXiv   pre-print
The increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence (AI) to brain data analysis, poses a challenge for ethics and  ...  This framework is aimed at maximizing the benefits of facilitated brain data collection and further processing for science and medicine whilst minimizing risks and preventing harmful use.  ...  Technical approaches to improve protection from leakage and unwarranted access include homomorphic encryption, multi-party computation, federated learning, and differential privacy 44 .  ... 
arXiv:2109.11960v2 fatcat:wmrde2ecfralnfjsififwmihhm

Distributed Differentially Private Computation of Functions with Correlated Noise [article]

Hafiz Imtiaz, Jafar Mohammadi, Anand D. Sarwate
2021 arXiv   pre-print
CAPE can be used in conjunction with the functional mechanism for statistical and machine learning optimization problems.  ...  Many applications of machine learning, such as human health research, involve processing private or sensitive information.  ...  CAPE can be employed in a wide range of computations that frequently appear in machine learning problems. • We propose an improved functional mechanism (FM) using a tighter sensitivity analysis.  ... 
arXiv:1904.10059v3 fatcat:rvtgmnq44jgl7mcbr7egepo7dy

CARS 2021: Computer Assisted Radiology and Surgery Proceedings of the 35th International Congress and Exhibition Munich, Germany, June 21–25, 2021

2021 International Journal of Computer Assisted Radiology and Surgery  
The University Rovira i Virgili also supports this work with project 2019PFR-B2-61. References  ...  Acknowledgements This work has been funded by the research project PI18/00169 from Instituto de Salud Carlos III & FEDER funds.  ...  All data analysis was performed using the Weka machine learning platform [2] .  ... 
doi:10.1007/s11548-021-02375-4 pmid:34085172 fatcat:6d564hsv2fbybkhw4wvc7uuxcy