Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data

Johan van Soest; Chang Sun; Ole Mussmann; Marco Puts; Bob van den Berg; Alexander Malic; Claudia van Oppen; David Townend; Andre Dekker; Michel Dumontier

doi:10.3233/978-1-61499-852-5-581

Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data

Johan van Soest^*, Chang Sun, Ole Mussmann, Marco Puts, Bob van den Berg, Alexander Malic, Claudia van Oppen, David Townend, Andre Dekker, Michel Dumontier

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

Conventional data mining algorithms are unable to satisfy the current requirements on analyzing big data in some fields such as medicine, policy making, judicial, and tax records. However, applying diverse datasets from different institutes (both healthcare and non-healthcare related) can enrich information and insights. So far, analyzing this data in an automated, privacy-preserving manner does not exist to our knowledge. In this work, we propose an infrastructure, and proof-of-concept for privacy-preserving analytics on vertically partitioned data.

Original language	English
Title of host publication	BUILDING CONTINENTS OF KNOWLEDGE IN OCEANS OF DATA: THE FUTURE OF CO-CREATED EHEALTH
Publisher	IOS Press
Pages	581-585
Number of pages	5
Volume	247
ISBN (Print)	9781614998518
DOIs	https://doi.org/10.3233/978-1-61499-852-5-581
Publication status	Published - 2018
Event	Conference on Medical Informatics Europe (MIE) - SWEDEN Duration: 24 Apr 2018 → 26 Apr 2018

Publication series

Series	Studies in Health Technology and Informatics
Volume	247
ISSN	0926-9630

Conference

Conference	Conference on Medical Informatics Europe (MIE)
Period	24/04/18 → 26/04/18

Keywords

Infrastructure
machine learning
data mining
statistics
privacy-preserving
secondary use of data
METHODOLOGY

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10.3233/978-1-61499-852-5-581

https://www.ncbi.nlm.nih.gov/pubmed/29678027

Cite this

van Soest, J., Sun, C., Mussmann, O., Puts, M., van den Berg, B., Malic, A., van Oppen, C., Townend, D., Dekker, A., & Dumontier, M. (2018). Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data. In BUILDING CONTINENTS OF KNOWLEDGE IN OCEANS OF DATA: THE FUTURE OF CO-CREATED EHEALTH (Vol. 247, pp. 581-585). IOS Press. https://doi.org/10.3233/978-1-61499-852-5-581

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booktitle = "BUILDING CONTINENTS OF KNOWLEDGE IN OCEANS OF DATA: THE FUTURE OF CO-CREATED EHEALTH",

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van Soest, J , Sun, C, Mussmann, O, Puts, M, van den Berg, B, Malic, A, van Oppen, C , Townend, D , Dekker, A & Dumontier, M 2018, Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data. in BUILDING CONTINENTS OF KNOWLEDGE IN OCEANS OF DATA: THE FUTURE OF CO-CREATED EHEALTH. vol. 247, IOS Press, Studies in Health Technology and Informatics, vol. 247, pp. 581-585, Conference on Medical Informatics Europe (MIE), 24/04/18. https://doi.org/10.3233/978-1-61499-852-5-581

Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data. / van Soest, Johan ; Sun, Chang; Mussmann, Ole et al.
BUILDING CONTINENTS OF KNOWLEDGE IN OCEANS OF DATA: THE FUTURE OF CO-CREATED EHEALTH. Vol. 247 IOS Press, 2018. p. 581-585 (Studies in Health Technology and Informatics, Vol. 247).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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AU - Malic, Alexander

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AU - Townend, David

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AU - Dumontier, Michel

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AB - Conventional data mining algorithms are unable to satisfy the current requirements on analyzing big data in some fields such as medicine, policy making, judicial, and tax records. However, applying diverse datasets from different institutes (both healthcare and non-healthcare related) can enrich information and insights. So far, analyzing this data in an automated, privacy-preserving manner does not exist to our knowledge. In this work, we propose an infrastructure, and proof-of-concept for privacy-preserving analytics on vertically partitioned data.

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KW - machine learning

KW - data mining

KW - statistics

KW - privacy-preserving

KW - secondary use of data

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van Soest J , Sun C, Mussmann O, Puts M, van den Berg B, Malic A et al. Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data. In BUILDING CONTINENTS OF KNOWLEDGE IN OCEANS OF DATA: THE FUTURE OF CO-CREATED EHEALTH. Vol. 247. IOS Press. 2018. p. 581-585. (Studies in Health Technology and Informatics, Vol. 247). doi: 10.3233/978-1-61499-852-5-581

Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data

Abstract

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Keywords

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