Blockchain technology and neural networks for the internet of medical things

Dawid Polap, Gautam Srivastava, Alireza Jolfaei, Reza M. Parizi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

31 Citations (Scopus)

Abstract

In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.

Original languageEnglish
Title of host publicationIEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
Place of PublicationOnline
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages508-513
Number of pages6
ISBN (Electronic)9781728186955, 9781728186962
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes
Event2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020 - Toronto, Canada
Duration: 6 Jul 20209 Jul 2020

Publication series

NameIEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020

Conference

Conference2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
Country/TerritoryCanada
CityToronto
Period6/07/209/07/20

Keywords

  • Blockchain
  • Federated Learning
  • Internet of Medical Things
  • Internet of Things
  • Neural Networks
  • Patient Data
  • Privacy
  • Security

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