The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Anonymity and Pseudonymity in Data-Driven Science
|
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
Purchase
|
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.
Related Content
Dina Darwish.
© 2024.
48 pages.
|
Dina Darwish.
© 2024.
51 pages.
|
Smrity Prasad, Kashvi Prawal.
© 2024.
19 pages.
|
Jignesh Patil, Sharmila Rathod.
© 2024.
17 pages.
|
Ganesh B. Regulwar, Ashish Mahalle, Raju Pawar, Swati K. Shamkuwar, Priti Roshan Kakde, Swati Tiwari.
© 2024.
23 pages.
|
Pranali Dhawas, Abhishek Dhore, Dhananjay Bhagat, Ritu Dorlikar Pawar, Ashwini Kukade, Kamlesh Kalbande.
© 2024.
24 pages.
|
Pranali Dhawas, Minakshi Ashok Ramteke, Aarti Thakur, Poonam Vijay Polshetwar, Ramadevi Vitthal Salunkhe, Dhananjay Bhagat.
© 2024.
26 pages.
|
|
|