Privacy-preserving data sharing via probabilistic modeling

Privacy-preserving data sharing via probabilistic modeling

Summary: 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...

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Bibliographic Details
Main Authors: Joonas Jälkö, Eemil Lagerspetz, Jari Haukka, Sasu Tarkoma, Antti Honkela, Samuel Kaski
Format: Article
Language:English
Published: Elsevier 2021-07-01
Series:Patterns
Subjects:
differential privacy
machine learning
probabilistic modeling
open data
synthetic data
Online Access:http://www.sciencedirect.com/science/article/pii/S2666389921000970

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http://www.sciencedirect.com/science/article/pii/S2666389921000970

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