Abstract

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.
Original languageUndefined
Title of host publicationSicherheit 2014: Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V. (GI)
Place of PublicationBonn
PublisherGesellschaft für Informatik
Pages481-500
Number of pages20
ISBN (Print)978-3-88579-622-0
StatePublished - 2014
EventSicherheit 2014 - Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V - Wien, Austria

Publication series

NameLecture Notes in Informatics (LNI)
PublisherGesellschaft für Informatik e.V.

Conference

ConferenceSicherheit 2014 - Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V
CountryAustria
CityWien
Period19/03/1421/03/14

Fingerprint

Transparency
Statistics
Experiments

Keywords

  • METIS-304102
  • IR-91145
  • SCS-Cybersecurity
  • EWI-24760

Cite this

Makri, E., Everts, M. H., de Hoogh, S., Peter, A., op den Akker, H., Hartel, P. H., & Jonker, W. (2014). Privacy-Preserving Verification of Clinical Research. In Sicherheit 2014: Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V. (GI) (pp. 481-500). (Lecture Notes in Informatics (LNI)). Bonn: Gesellschaft für Informatik.

Makri, E.; Everts, Maarten Hinderik; de Hoogh, Sebastiaan; Peter, Andreas; op den Akker, Harm; Hartel, Pieter H.; Jonker, Willem / Privacy-Preserving Verification of Clinical Research.

Sicherheit 2014: Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V. (GI). Bonn : Gesellschaft für Informatik, 2014. p. 481-500 (Lecture Notes in Informatics (LNI)).

Research output: Scientific - peer-reviewConference contribution

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abstract = "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.",
keywords = "METIS-304102, IR-91145, SCS-Cybersecurity, EWI-24760",
author = "E. Makri and Everts, {Maarten Hinderik} and {de Hoogh}, Sebastiaan and Andreas Peter and {op den Akker}, Harm and Hartel, {Pieter H.} and Willem Jonker",
year = "2014",
isbn = "978-3-88579-622-0",
series = "Lecture Notes in Informatics (LNI)",
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Makri, E, Everts, MH, de Hoogh, S, Peter, A, op den Akker, H, Hartel, PH & Jonker, W 2014, Privacy-Preserving Verification of Clinical Research. in Sicherheit 2014: Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V. (GI). Lecture Notes in Informatics (LNI), Gesellschaft für Informatik, Bonn, pp. 481-500, Sicherheit 2014 - Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V, Wien, Austria, 19-21 March.

Privacy-Preserving Verification of Clinical Research. / Makri, E.; Everts, Maarten Hinderik; de Hoogh, Sebastiaan; Peter, Andreas; op den Akker, Harm; Hartel, Pieter H.; Jonker, Willem.

Sicherheit 2014: Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V. (GI). Bonn : Gesellschaft für Informatik, 2014. p. 481-500 (Lecture Notes in Informatics (LNI)).

Research output: Scientific - peer-reviewConference contribution

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T1 - Privacy-Preserving Verification of Clinical Research

AU - Makri,E.

AU - Everts,Maarten Hinderik

AU - de Hoogh,Sebastiaan

AU - Peter,Andreas

AU - op den Akker,Harm

AU - Hartel,Pieter H.

AU - Jonker,Willem

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N2 - 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.

AB - 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.

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KW - IR-91145

KW - SCS-Cybersecurity

KW - EWI-24760

M3 - Conference contribution

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T3 - Lecture Notes in Informatics (LNI)

SP - 481

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BT - Sicherheit 2014: Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V. (GI)

PB - Gesellschaft für Informatik

ER -

Makri E, Everts MH, de Hoogh S, Peter A, op den Akker H, Hartel PH et al. Privacy-Preserving Verification of Clinical Research. In Sicherheit 2014: Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 7. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V. (GI). Bonn: Gesellschaft für Informatik. 2014. p. 481-500. (Lecture Notes in Informatics (LNI)).