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Partial commitment – "Try before you buy" and "Buyer’s remorse" for personal data in Big Data & Machine learning
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (PriSec)
2017 (English)In: 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, p. 3-11Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2017. Vol. 505, p. 3-11
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868-422X ; 505
Keywords [en]
Big Data, Machine learning, data sharing, personal information, information privacy, commitment, consent, data processing, user interface, interaction
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-55017DOI: 10.1007/978-3-319-59171-1ISI: 000432194900001ISBN: 978-3-319-59170-4 (print)ISBN: 978-3-319-59171-1 (electronic)OAI: oai:DiVA.org:kau-55017DiVA, id: diva2:1109462
Conference
11th IFIP WG 11.11 International Conference on Trust Management, IFIPTM 2017
Available from: 2017-06-14 Created: 2017-06-14 Last updated: 2018-05-31Bibliographically approved

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Fritsch, Lothar

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CiteExportLink to record
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Citation style
  • apa
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