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Privacy-preserving data sharing via probabilistic modeling

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Jälkö, Joonas
dc.contributor.author Lagerspetz, Eemil
dc.contributor.author Haukka, Jari
dc.contributor.author Tarkoma, Sasu
dc.contributor.author Honkela, Antti
dc.contributor.author Kaski, Samuel
dc.date.accessioned 2021-08-04T06:42:52Z
dc.date.available 2021-08-04T06:42:52Z
dc.date.issued 2021-07-09
dc.identifier.citation Jälkö , J , Lagerspetz , E , Haukka , J , Tarkoma , S , Honkela , A & Kaski , S 2021 , ' Privacy-preserving data sharing via probabilistic modeling ' , Patterns , vol. 2 , no. 7 , 100271 . https://doi.org/10.1016/j.patter.2021.100271 en
dc.identifier.issn 2666-3899
dc.identifier.other PURE UUID: 8db68883-28ba-430c-95e8-bac6287a2f7d
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/8db68883-28ba-430c-95e8-bac6287a2f7d
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85109442433&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/65991672/Privacy_preserving_data_sharing_via_probabilistic_modeling.PIIS2666389921000970.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/108915
dc.description Funding Information: This work was supported by the Academy of Finland (grants 325573 , 325572 , 319264 , 313124 , 303816 , 303815 , 297741 , and 292334 and the Flagship program Finnish Center for Artificial Intelligence [FCAI]). We thank the Carat group for access to the Carat data ( http://carat.cs.helsinki.fi/ ) and the CARING study group ( https://www.caring-diabetes.eu/ ) for access to the ARD data. Publisher Copyright: © 2021 The Authors
dc.description.abstract Differential privacy allows quantifying privacy loss resulting from accession of sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation but would leave open the problem of designing what kind of synthetic data. We propose formulating the problem of private data release through probabilistic modeling. This approach transforms the problem of designing the synthetic data into choosing a model for the data, allowing also the inclusion of prior knowledge, which improves the quality of the synthetic data. We demonstrate empirically, in an epidemiological study, that statistical discoveries can be reliably reproduced from the synthetic data. We expect the method to have broad use in creating high-quality anonymized data twins of key datasets for research. en
dc.format.extent 10
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Elsevier
dc.relation.ispartofseries Patterns en
dc.relation.ispartofseries Volume 2, issue 7 en
dc.rights openAccess en
dc.title Privacy-preserving data sharing via probabilistic modeling en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department University of Helsinki
dc.contributor.department Probabilistic Machine Learning
dc.subject.keyword differential privacy
dc.subject.keyword DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem
dc.subject.keyword machine learning
dc.subject.keyword open data
dc.subject.keyword probabilistic modeling
dc.subject.keyword synthetic data
dc.identifier.urn URN:NBN:fi:aalto-202108048159
dc.identifier.doi 10.1016/j.patter.2021.100271
dc.type.version publishedVersion

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