Privacy-Preserving Edge-Cloud Architecture for IoT Healthcare Systems

Simple item page
dc.contributor.advisorLitoiu, Marin
dc.contributor.authorGoyal, Payal
dc.date.accessioned2021-11-15T15:18:39Z
dc.date.available2021-11-15T15:18:39Z
dc.date.copyright2021-05
dc.date.issued2021-11-15
dc.date.updated2021-11-15T15:18:39Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractWith the surging demand for Internet of Things (IoT) healthcare applications, a myriad of data privacy concerns come to light. Cloud computing inherits the risks of exposing data to re-identification vulnerabilities. A secure solution is storing and processing data locally on edge, but it lacks the provision of powerful machine learning (ML) needs. An improved computing framework is required to incorporate ML capabilities and user-data confidentiality. We perform a systematic study of IoT healthcare systems and propose a three-tier architecture that protects and enables data sharing. The edge anonymizes data using differential privacy (DP); transmits it to the cloud to train ML classifier; sent back trained classifier to edge to make inferences. Our findings show 1) XgBoost classifier performs relatively well; classifiers' accuracy trained using DP data is close to that of original data 2) Round-trip execution performance of architecture shows high average mean and variance with higher privacy budgets.
dc.identifier.urihttp://hdl.handle.net/10315/38662
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectInformation technology
dc.subject.keywordsData privacy
dc.subject.keywordsDifferential privacy
dc.subject.keywordsData anonymization
dc.subject.keywordsMachine learning
dc.subject.keywordsPrivacy-preserving
dc.subject.keywordsEdge
dc.subject.keywordsCloud
dc.subject.keywordsHealthcare
dc.subject.keywordsIoT
dc.subject.keywordsClassification
dc.subject.keywordsNon-perturbative masking
dc.subject.keywordsStatistical disclosure control
dc.subject.keywordsData transformation
dc.subject.keywordsArchitecture
dc.subject.keywordsData sharing
dc.subject.keywordsPrivacy model
dc.subject.keywordsThree-tier
dc.subject.keywordsSystematic study
dc.titlePrivacy-Preserving Edge-Cloud Architecture for IoT Healthcare Systems
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Goyal_Payal_2021_Masters.pdf
Size:
1.84 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.39 KB
Format:
Plain Text
Description:
Download

Collections

Information Systems and Technology

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.