Towards privacy aware data analysis workflows for e-Science

Abstract

e-Science is getting more distributed and collaborative and data privacy quickly becomes a major concern, especially when the data contain sensitive information. Existing data access policies for privacy management are too restrictive for supporting the large variety of data analysis needs in e-Science. In this paper, we argue the need of a new type of policies that govern data privacy based on the type of processing done on the data. A semantic workflow approach is proposed to address the challenge. Data analysis processes are described as workflows. Ontologies for data analysis and privacy preservation describe the functionalities and the privacy attributes of the processes, as well as process-constraining privacy policies. We give some examples of related policies with their potential fields for application explained. Also, we present via a case study on distributed data clustering to illustrate how the approach could be integrated with a workflow system to make it privacy aware. Copyright © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.