Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data

Simple item page
dc.contributor.authorAfrose, Sharminen
dc.contributor.authorYao, Danfeng (Daphne)en
dc.contributor.authorKotevska, Oliveraen
dc.date.accessioned2022-03-03T15:10:15Zen
dc.date.available2022-03-03T15:10:15Zen
dc.date.issued2021-01-01en
dc.date.updated2022-03-03T15:10:03Zen
dc.description.abstractVarious Internet of Things (IoT) devices generate complex, dynamically changed, and infinite data streams. Adversaries can cause harm if they can access the user's sensitive raw streaming data. For this reason, protecting the privacy of the data streams is crucial. In this paper, we explore local differential privacy techniques for streaming data. We compare the techniques and report the advantages and limitations. We also present the effect on component (e.g., smoother, perturber) variations of distribution-based local differential privacy. We find that combining distribution-based noise during perturbation provides more flexibility to the interested entity.en
dc.description.versionPublished versionen
dc.format.extentPages 1-10en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/PST52912.2021.9647839en
dc.identifier.isbn9781665401845en
dc.identifier.orcidYao, Danfeng [0000-0001-8969-2792]en
dc.identifier.urihttp://hdl.handle.net/10919/109016en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsPublic Domainen
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.titleMeasurement of Local Differential Privacy Techniques for IoT-based Streaming Dataen
dc.title.serial2021 18th International Conference on Privacy, Security and Trust, PST 2021en
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.dcmitypeTexten
dc.type.otherConference Proceedingen
pubs.finish-date2021-12-15en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.start-date2021-12-13en

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
PST2021_IEEE_Xplore_compliant.pdf
Size:
2.3 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

All Faculty Deposits
Destination Area: Integrated Security (IS)
Scholarly Works, Computer Science