Privacy in Data Service Composition
| Autor: | Charith Perera, Christine Bonnet, Mahmoud Barhamgi, David Camacho, Djamal Benslimane, Chia-Mu Yu |
|---|---|
| Přispěvatelé: | Service Oriented Computing (SOC), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), The Open University [Milton Keynes] (OU), Universidad Autonoma de Madrid (UAM), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Information Systems and Management Delegate Computer Science - Cryptography and Security Computer Networks and Communications Computer science Privacy policy 02 engineering and technology Encryption computer.software_genre Computer security Computer Science - Databases Application domain 020204 information systems Health care 0202 electrical engineering electronic engineering information engineering Information system [INFO]Computer Science [cs] ComputingMilieux_MISCELLANEOUS Data collection business.industry Databases (cs.DB) Computer Science Applications Computer Science - Distributed Parallel and Cluster Computing Hardware and Architecture 020201 artificial intelligence & image processing Data as a service Distributed Parallel and Cluster Computing (cs.DC) business computer Cryptography and Security (cs.CR) Data integration |
| Zdroj: | IEEE Transactions on Services Computing IEEE Transactions on Services Computing, IEEE, In press, pp.1-1. ⟨10.1109/TSC.2019.2963309⟩ |
| ISSN: | 1939-1374 |
| DOI: | 10.1109/TSC.2019.2963309⟩ |
| Popis: | In modern information systems different information features, about the same individual, are often collected and managed\ud by autonomous data collection services that may have different privacy policies. Answering many end-users’ legitimate queries requires\ud the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often\ud called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this paper, we\ud propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted\ud mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted,\ud and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new\ud privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users,\ud in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the\ud healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives. |
| Databáze: | OpenAIRE |
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