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Graph Representation and Anonymization in Large Survey Rating Data

Graph Representation and Anonymization in Large Survey Rating Data

Xiaoxun Sun, Min Li
Copyright: © 2012 |Pages: 19
ISBN13: 9781613500538|ISBN10: 161350053X|EISBN13: 9781613500545
DOI: 10.4018/978-1-61350-053-8.ch014
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MLA

Sun, Xiaoxun, and Min Li. "Graph Representation and Anonymization in Large Survey Rating Data." Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, IGI Global, 2012, pp. 315-333. https://doi.org/10.4018/978-1-61350-053-8.ch014

APA

Sun, X. & Li, M. (2012). Graph Representation and Anonymization in Large Survey Rating Data. In S. Sakr & E. Pardede (Eds.), Graph Data Management: Techniques and Applications (pp. 315-333). IGI Global. https://doi.org/10.4018/978-1-61350-053-8.ch014

Chicago

Sun, Xiaoxun, and Min Li. "Graph Representation and Anonymization in Large Survey Rating Data." In Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, 315-333. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-053-8.ch014

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Abstract

We study the challenges of protecting privacy of individuals in the large public survey rating data in this chapter. Recent study shows that personal information in supposedly anonymous movie rating records is 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 can effectively prevent such breaches in large survey rating data sets. We tackle the problem by defining a principle called (k, e)-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 e. The (k, e)-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, e) requirements. The methods are applied to two real-life data sets to demonstrate their efficiency and practical utility.

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