Validating privacy requirements in large survey rating data
Edited book (chapter)
Chapter Title | Validating privacy requirements in large survey rating data |
---|---|
Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 3337 |
Book Title | Next generation data technologies for collective computational intelligence |
Authors | Sun, Xiaoxun (Author), Wang, Hua (Author) and Li, Jiuyong (Author) |
Editors | Bessis, Nik and Xhafa, Fatos |
Page Range | 445-469 |
Series | Studies in Computational Intelligence |
Chapter Number | 17 |
Number of Pages | 25 |
Year | 2011 |
Publisher | Springer |
Place of Publication | Berlin, Germany |
ISBN | 9783642203435 |
ISSN | 1860-949X |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-642-20344-2_17 |
Web Address (URL) | http://www.springerlink.com/content/m2p1828m61050114/ |
Abstract | Recent study shows that supposedly anonymous movie rating records are de-identified by using a little auxiliary information. In this chapter, we study a problem of protecting privacy of individuals in large public survey rating data. Such rating data usually contains both ratings of sensitive and non-sensitive issues, and the ratings of sensitive issues belong to personal privacy. Even when survey participants do not reveal any of their ratings, their survey records are potentially identifiable by using information from other public sources. To amend this, in this chapter, we propose a novel (k;e; l)-anonymity model to protect privacy in large survey rating data, in which each survey record is required to be 'similar' with at least k−1 others based on the non-sensitive ratings, where the similarity is controlled by e, and the standard deviation of sensitive ratings is at least l. We study an interesting yet non-trivial satisfaction problem of the proposed model, which is to decide whether a survey rating data set satisfies the privacy requirements given by the user. For this problem, we investigate its inherent properties theoretically, and devise a novel slice |
Keywords | privacy; requirements; survey rating data |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
460499. Cybersecurity and privacy not elsewhere classified | |
460905. Information systems development methodologies and practice | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Australian Council for Educational Research, Australia |
Department of Mathematics and Computing | |
University of South Australia | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q078w/validating-privacy-requirements-in-large-survey-rating-data
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