A Data Protection Impact Assessment Methodology for Cloud

  • Rehab Alnemr
  • Erdal Cayirci
  • Lorenzo Dalla Corte
  • Alexandr Garaga
  • Ronald Leenes
  • Rodney Mhungu
  • Siani Pearson
  • Chris Reed
  • Anderson Santana de Oliveira
  • Dimitra Stefanatou
  • Katerina Tetrimida
  • Asma Vranaki
Conference paper

DOI: 10.1007/978-3-319-31456-3_4

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9484)
Cite this paper as:
Alnemr R. et al. (2016) A Data Protection Impact Assessment Methodology for Cloud. In: Berendt B., Engel T., Ikonomou D., Le Métayer D., Schiffner S. (eds) Privacy Technologies and Policy. APF 2015. Lecture Notes in Computer Science, vol 9484. Springer, Cham

Abstract

We propose a data protection impact assessment (DPIA) method based on successive questionnaires for an initial screening and for a full screening for a given project. These were tailored to satisfy the needs of Small and Medium Enterprises (SMEs) that intend to process personal data in the cloud. The approach is based on legal and socio-economic analysis of privacy issues for cloud deployments and takes into consideration the new requirements for DPIAs within the European Union (EU) as put forward by the proposed General Data Protection Regulation (GDPR). The resultant features have been implemented within a tool.

Keywords

Data protection impact assessment EU GDPR Cloud Privacy 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Rehab Alnemr
    • 1
  • Erdal Cayirci
    • 2
  • Lorenzo Dalla Corte
    • 3
  • Alexandr Garaga
    • 4
  • Ronald Leenes
    • 3
  • Rodney Mhungu
    • 3
  • Siani Pearson
    • 1
  • Chris Reed
    • 5
  • Anderson Santana de Oliveira
    • 4
  • Dimitra Stefanatou
    • 3
  • Katerina Tetrimida
    • 3
  • Asma Vranaki
    • 5
  1. 1.HP LabsBristolUK
  2. 2.Stavanger UniversityStavangerNorway
  3. 3.Tilburg UniversityTilburgThe Netherlands
  4. 4.SAP LabsMouginsFrance
  5. 5.Queen Mary University of LondonLondonUK

Personalised recommendations