• DocumentCode
    2368615
  • Title

    Scalable, accountable privacy management for large organizations

  • Author

    Pearson, Siani ; Rao, Prasad ; Sander, Tomas ; Parry, Alan ; Paull, Allan ; Patruni, Satish ; Dandamudi-Ratnakar, Venkata ; Sharma, Pranav

  • Author_Institution
    HP Labs., Bristol, UK
  • fYear
    2009
  • fDate
    1-4 Sept. 2009
  • Firstpage
    168
  • Lastpage
    175
  • Abstract
    Accountability is emerging as an important theme within the regulatory privacy community. For global corporations, demonstrating accountability is no easy task due to the potentially large number of projects that have privacy sensitive aspects, privacy oversight being a mostly manual process and privacy staff typically being small. So how can a company present proof points that its projects comply with its privacy promises and obligations? In this paper we address this problem by introducing a technology based solution for scalable, accountable privacy management across an organization. We present an Accountability Model Tool (AMT) that addresses the problem of capturing data about business processes in order to determine their privacy compliance. AMT utilizes an intelligent questionnaire with good completeness properties and is based on an augmented rule engine.
  • Keywords
    DP management; business data processing; organisational aspects; security of data; accountability model tool; accountable privacy management; augmented rule engine; business process; regulatory privacy community; scalable privacy management; technology based solution; Australia; Companies; Data privacy; Engines; Global communication; International collaboration; Law; Legal factors; Technology management; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Enterprise Distributed Object Computing Conference Workshops, 2009. EDOCW 2009. 13th
  • Conference_Location
    Auckland
  • Print_ISBN
    978-1-4244-5563-8
  • Type

    conf

  • DOI
    10.1109/EDOCW.2009.5331996
  • Filename
    5331996