Privacy-preserving verification of clinical research
| Autor: | Makri, E., Everts, M. H., Hoogh, S., Peter, A., Op Den Akker, H., Pieter Hartel, Jonker, W. |
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| Přispěvatelé: | Databases (Former) |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Infostructures
Aggregates Communication & Information TS - Technical Sciences Statistical calculations SCS-Cybersecurity EWI-24760 ISEC - Information Security Clinical researchers Information Society METIS-304102 Hospital data processing Clinical research Privacy preserving IR-91145 Cryptography Sensitive data Secure multi-party computation Safety Data privacy |
| Zdroj: | Katzenbeisser, S.Lotz, V.Weippl, E., Lecture Notes in Informatics (LNI), Proceedings-Series of the Gesellschaft fur Informatik (GI), P-228, 481-500 Sicherheit 2014: Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V. (GI), 481-500 STARTPAGE=481;ENDPAGE=500;TITLE=Sicherheit 2014: Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V. (GI) Scopus-Elsevier |
| Popis: | We 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. |
| Databáze: | OpenAIRE |
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