caa a22 4500 445871261 CHVBK 20180317145512.0 cr unu---uuuuu 170323e20111101xx s 000 0 eng 10.1007/s10618-010-0208-4 doi (NATIONALLICENCE)springer-10.1007/s10618-010-0208-4 Publishing anonymous survey rating data [Elektronische Daten] [Xiaoxun Sun, Hua Wang, Jiuyong Li, Jian Pei] We study the challenges of protecting privacy of individuals in the large public survey rating data in this paper. Recent study shows that personal information in supposedly anonymous movie rating records are de-identified. The survey rating data usually contains both ratings of sensitive and non-sensitive issues. The ratings of sensitive issues involve personal privacy. Even though the survey participants do not reveal any of their ratings, their survey records are potentially identifiable by using information from other public sources. None of the existing anonymisation principles (e.g., k-anonymity, l-diversity, etc.) can effectively prevent such breaches in large survey rating data sets. We tackle the problem by defining a principle called $${(k,\epsilon)}$$-anonymity model to protect privacy. Intuitively, the principle requires that, for each transaction t in the given survey rating data T, at least (k − 1) other transactions in T must have ratings similar to t, where the similarity is controlled by $${\epsilon}$$ . The $${(k,\epsilon)}$$ -anonymity model is formulated by its graphical representation and a specific graph-anonymisation problem is studied by adopting graph modification with graph theory. Various cases are analyzed and methods are developed to make the updated graph meet $${(k,\epsilon)}$$ requirements. The methods are applied to two real-life data sets to demonstrate their efficiency and practical utility. The Author(s), 2010 $${(k\epsilon)}$$ -anonymity nationallicence Survey rating data nationallicence Graphical representation nationallicence Sun Xiaoxun Australian Council for Educational Research, 19 Prospect Hill Road, Camberwell, VIC, Australia aut Wang Hua Department of Mathematics Computing, University of Southern Queensland, Toowoomba, QLD, Australia aut Li Jiuyong School of Computer and Information Science, University of South Australia, Adelaide, SA, Australia aut Pei Jian School of Computing Science, Simon Fraser University, Burnaby, BC, Canada aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/3(2011-11-01), 379-406 1384-5810 23:3<379 2011 23 10618 https://doi.org/10.1007/s10618-010-0208-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0208-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sun Xiaoxun Australian Council for Educational Research, 19 Prospect Hill Road, Camberwell, VIC, Australia aut NATIONALLICENCE 700 1- Wang Hua Department of Mathematics Computing, University of Southern Queensland, Toowoomba, QLD, Australia aut NATIONALLICENCE 700 1- Li Jiuyong School of Computer and Information Science, University of South Australia, Adelaide, SA, Australia aut NATIONALLICENCE 700 1- Pei Jian School of Computing Science, Simon Fraser University, Burnaby, BC, Canada aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/3(2011-11-01), 379-406 1384-5810 23:3<379 2011 23 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer