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
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