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Cloud Challenge: Secure End-to-End Processing of Smart Metering Data

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

Dokumenter

DOI

  • Andrey Brito, Universidade Federal de Campina Grande, Brasilien
  • Christof Fetzer, Technische Universität Dresden, Tyskland
  • Stefan Köpsell, Technische Universität Dresden, Tyskland
  • Peter Pietzuch, Imperial College London, Storbritannien
  • Marcelo Pasin, University of Neuchâtel, Schweiz
  • Pascal Felber, University of Neuchâtel, Schweiz
  • Keiko Fonseca, Universidade Tecnologica Federal do Parana, Brasilien
  • Marcelo Rosa, Universidade Tecnologica Federal do Parana, Brasilien
  • Rodrigo Riella, LACTAC, Brasilien
  • Charles Prado, INMETRO, Brasilien
  • Luiz F.R. da Costa Carmo, INMETRO, Brasilien
  • Daniel Enrique Lucani Rötter
  • Marton A. Sipos, Chocolate Cloud ApS
    • ,
  • Laszlo Nagy, Chocolate Cloud ApS
    • ,
  • Marcell Feher, Chocolate Cloud ApS, Danmark

Cloud computing considerably reduces the costs of deploying applications through on-demand, automated, and fine-granular allocation of resources. Even in private settings, cloud computing platforms enable agile and self-service management, which means that physical resources are shared more efficiently. Nevertheless, using shared infrastructures also creates more opportunities for attacks and data breaches. In this paper, we describe the SecureCloud approach. The SecureCloud project aims to enable confidentiality and integrity of data and applications running in potentially untrusted cloud environments. The project leverages technologies such as Intel SGX, OpenStack and Kubernetes to provide a cloud platform that supports secure applications. In addition, the project provides tools that help generating cloud-native, secure applications and services to be deployed on potentially untrusted clouds. The results have been validated in the smart grid scenario and enabled a data workflow that is protected end-To-end: from the collection of data to the generation of high-level information such as fraud alerts.

OriginalsprogEngelsk
TitelProceedings - 11th IEEE/ACM International Conference on Utility and Cloud Computing Companion, UCC Companion 2018
Antal sider7
ForlagIEEE
Udgivelsesår2018
Sider36-42
Artikelnummer8605754
ISBN (Elektronisk)9781728103594
DOI
StatusUdgivet - 2018
Eksternt udgivetJa

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