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dc.contributor.authorMakri, Eleftheria
dc.contributor.authorEverts, Maarten H.
dc.contributor.authorHoogh, Sebastiaan De
dc.contributor.authorPeter, Andreas
dc.contributor.authorAkker, Harm Op Den
dc.contributor.authorHartel, Pieter
dc.contributor.authorJonker, Willem
dc.contributor.editorKatzenbeisser, Stefan
dc.contributor.editorLotz, Volkmar
dc.contributor.editorWeippl, Edgar
dc.date.accessioned2019-01-25T14:17:30Z
dc.date.available2019-01-25T14:17:30Z
dc.date.issued2014
dc.identifier.isbn978-3-88579-622-0
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/20065
dc.description.abstractWe treat the problem of privacy-preserving statistics verification in clinical research. We show that given aggregated results from statistical calculations, we can verify their correctness efficiently, without revealing any of the private inputs used for the calculation. Our construction is based on the primitive of Secure Multi-Party Computation from Shamir's Secret Sharing. Basically, our setting involves three parties: a hospital, which owns the private inputs, a clinical researcher, who lawfully processes the sensitive data to produce an aggregated statistical result, and a third party (usually several verifiers) assigned to verify this result for reliability and transparency reasons. Our solution guarantees that these verifiers only learn about the aggregated results (and what can be inferred from those about the underlying private data) and nothing more. By taking advantage of the particular scenario at hand (where certain intermediate results, e.g., the mean over the dataset, are available in the clear) and utilizing secret sharing primitives, our approach turns out to be practically efficient, which we underpin by performing several experiments on real patient data. Our results show that the privacy-preserving verification of the most commonly used statistical operations in clinical research presents itself as an important use case, where the concept of secure multi-party computation becomes employable in practice.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSicherheit 2014 – Sicherheit, Schutz und Zuverlässigkeit
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-264
dc.titlePrivacy-preserving verification of clinical researchen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages481-500
mci.conference.sessiontitleRegular Research Papers
mci.conference.locationWien, Österreich
mci.conference.date19.-21. März 2014


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