Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Partial commitment – "Try before you buy" and "Buyer’s remorse" for personal data in Big Data & Machine learning
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). (PriSec)ORCID-id: 0000-0002-0418-4121
2017 (engelsk)Inngår i: Trust Management XI: 11th IFIP WG 11.11 International Conference, IFIPTM 2017, Gothenburg, Sweden, June 12-16, 2017, Proceedings / [ed] Jan-Phillip Steghöfer, Babak Esfandiari, Cham, Switzerland: Springer, 2017, Vol. 505, s. 3-11Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The concept of partialcommitment is discussed in the context of personal privacy management in datascience. Uncommitted, promiscuous or partially committed user’s data may eitherhave a negative impact on model or data quality, or it may impose higherprivacy compliance cost on data service providers. Many Big Data (BD) andMachine Learning (ML) scenarios involve the collection and processing of largevolumes of person-related data. Data is gathered about many individuals as wellas about many parameters in individuals. ML and BD both spend considerable resourceson model building, learning, and data handling. It is therefore important toany BD/ML system that the input data trained and processed is of high quality,represents the use case, and is legally processes in the system. Additionalcost is imposed by data protection regulation with transparency, revocation andcorrection rights for data subjects. Data subjects may, for several reasons, only partially accept a privacypolicy, and chose to opt out, request data deletion or revoke their consent fordata processing. This article discusses the concept of partial commitment andits possible applications from both the data subject and the data controllerperspective in Big Data and Machine Learning.

sted, utgiver, år, opplag, sider
Cham, Switzerland: Springer, 2017. Vol. 505, s. 3-11
Serie
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868-422X ; 505
Emneord [en]
Big Data, Machine learning, data sharing, personal information, information privacy, commitment, consent, data processing, user interface, interaction
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:kau:diva-55017DOI: 10.1007/978-3-319-59171-1ISI: 000432194900001ISBN: 978-3-319-59170-4 (tryckt)ISBN: 978-3-319-59171-1 (digital)OAI: oai:DiVA.org:kau-55017DiVA, id: diva2:1109462
Konferanse
11th IFIP WG 11.11 International Conference on Trust Management, IFIPTM 2017
Tilgjengelig fra: 2017-06-14 Laget: 2017-06-14 Sist oppdatert: 2019-07-11bibliografisk kontrollert

Open Access i DiVA

Fulltext(231 kB)250 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 231 kBChecksum SHA-512
68616a37720c0231e62b9d447e3fc6a72cba9854883ad2fcb5170ada65a2c65b301bc06c5667d585b3f3659c5fd9a0dd5ff9003386077c2dddea48f8bb36b0ed
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekst

Person

Fritsch, Lothar

Søk i DiVA

Av forfatter/redaktør
Fritsch, Lothar
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 250 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 594 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf