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
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;
Conference_Titel :
Enterprise Distributed Object Computing Conference Workshops, 2009. EDOCW 2009. 13th
Conference_Location :
Auckland
Print_ISBN :
978-1-4244-5563-8
DOI :
10.1109/EDOCW.2009.5331996