Secure distributed queries over large sets of personal home boxes
| Autor: | Nicolas Anciaux, Guillaume Scerri, Philippe Pucheral, Riad Ladjel |
|---|---|
| Přispěvatelé: | Ladjel, Riad, Personal Trusted cloud (PETRUS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Données et algorithmes pour une ville intelligente et durable - DAVID (DAVID), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Information privacy
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] Secure Distributed Computing Computer science business.industry Computation Data management Data Privacy Control (management) Trusted Execution Environment Fault tolerance 02 engineering and technology [INFO] Computer Science [cs] Computer security computer.software_genre Field (computer science) Digital Life 020204 information systems 0202 electrical engineering electronic engineering information engineering [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB] [INFO]Computer Science [cs] 020201 artificial intelligence & image processing business computer |
| Zdroj: | Transactions on Large-Scale Data-and Knowledge-Centered Systems XLIV ISBN: 9783662622704 Transactions on Large-Scale Data-and Knowledge-Centered Systems Transactions on Large-Scale Data-and Knowledge-Centered Systems, 2020 Transactions on Large-Scale Data-and Knowledge-Centered Systems, Springer Berlin / Heidelberg, 2020 |
| ISSN: | 1869-1994 |
| Popis: | International audience; 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 ar-chitectures 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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