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Anonymity and Pseudonymity in Data-Driven Science
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Author(s): Heidelinde Hobel (SBA Research, Austria), Sebastian Schrittwieser (St. Poelten University of Applied Sciences, Austria), Peter Kieseberg (SBA Research, Austria)and Edgar Weippl (Vienna University of Technology and SBA Research, Austria)
Copyright: 2014
Pages: 7
Source title:
Encyclopedia of Business Analytics and Optimization
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-4666-5202-6.ch013
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Abstract
Data Mining, Business Intelligence and other empirical approaches have great potential for various fields of research activities considering the improvements in data processing and retrieval. By using an empirical approach for the research methodology, the underlying data set should be available, at least for the review process in order to prevent fraud and ensure quality of research. However, data disclosure of the research data raises considerable privacy concerns due to the liability of the scholars to protect the privacy of their volunteers and adhere to the privacy policies of protected data. Therefore it is important to know about the strengths and weaknesses of existing approaches of anonymization, pseudonymization and attacks such as inference attacks. This chapter will provide an overview.
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