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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Secure Distributed Queries over Large Sets of Personal Home Boxes

verfasst von : Riad Ladjel, Nicolas Anciaux, Philippe Pucheral, Guillaume Scerri

Erschienen in: Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV

Verlag: Springer Berlin Heidelberg

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Abstract

Smart disclosure initiatives and new regulations such as GDPR allow individuals to get the control back on their data by gathering their entire digital life in a Personal Data Management Systems (PDMS). Multiple PDMS architectures exist and differ on their ability to preserve data privacy and to perform collective computations crossing data of multiple individuals (e.g., epidemiological or social studies) but none of them satisfy both objectives. The emergence of Trusted Execution Environments (TEE) changes the game. We propose a solution called Trusted PDMS, combining the TEE and PDMS properties to manage the data of each individual, and a complete framework to execute collective computation on top of them, with strong privacy and fault tolerance guarantees. We demonstrate the practicality of the solution through a real case-study being conducted over 10.000 patients in the healthcare field.

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Vorheriges Kapitel From Task Tuning to Task Assignment in Privacy-Preserving Crowdsourcing Platforms
Nächstes Kapitel Evaluating Classification Feasibility Using Functional Dependencies
Fußnoten
1
A k-equipartition of a set is the partitioning of this set in partitions of cardinality k.
 
2
We assume that each node is endowed with a public/private key pair.
 
3
Note that this formally makes the communication flow data dependent as the chosen \( k^{\prime } \) depends on the data sent to each scrambler. This, however, only leaks information on the distribution of data, not on any individual data. We do not view this as a significant threat.
 
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Metadaten
Titel
Secure Distributed Queries over Large Sets of Personal Home Boxes
verfasst von
Riad Ladjel
Nicolas Anciaux
Philippe Pucheral
Guillaume Scerri
Copyright-Jahr
2020
Verlag
Springer Berlin Heidelberg
Buch
Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV

Print ISBN: 978-3-662-62270-4

Electronic ISBN: 978-3-662-62271-1

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-662-62271-1_4