Privacy-preserving data sharing via probabilistic modeling

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Jalko , 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

Title: Privacy-preserving data sharing via probabilistic modeling
Author: Jalko, Joonas; Lagerspetz, Eemil; Haukka, Jari; Tarkoma, Sasu; Honkela, Antti; Kaski, Samuel
Contributor organization: Department of Mathematics and Statistics
Department of Computer Science
HUS Abdominal Center
Department of Public Health
Content-Centric Structures and Networking research group / Sasu Tarkoma
Helsinki Institute for Information Technology
Probabilistic Mechanistic Models for Genomics research group / Antti Honkela
Date: 2021-07-09
Language: eng
Number of pages: 10
Belongs to series: Patterns
ISSN: 2666-3899
DOI: https://doi.org/10.1016/j.patter.2021.100271
URI: http://hdl.handle.net/10138/333081
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.
Subject: NOISE
113 Computer and information sciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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