On the identity anonymization of high-dimensional rating data
Article
Article Title | On the identity anonymization of high-dimensional rating data |
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ERA Journal ID | 17819 |
Article Category | Article |
Authors | Sun, Xiaoxun (Author), Wang, Hua (Author) and Zhang, Yanchun (Author) |
Journal Title | Concurrency and Computation: Practice and Experience |
Journal Citation | 24 (10), pp. 1108-1122 |
Number of Pages | 15 |
Year | 2012 |
Publisher | John Wiley & Sons |
Place of Publication | United Kingdom |
ISSN | 1532-0626 |
1532-0634 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/cpe.1724 |
Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1002/cpe.1724 |
Abstract | We study the challenges of protecting the privacy of individuals in a large public survey rating data. The survey rating data usually contains both ratings of sensitive and non-sensitive issues. The ratings of sensitive issues involve personal privacy. Although 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 anonymization principles (e.g. k-anonymity, l-diversity, etc.) can effectively prevent such breaches in large survey rating data sets. In this paper, we tackle the problem by defining a principle called (k, epsilon, l)-anonymity. 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 ε and the standard deviation of sensitive ratings is at least l. We propose a greedy approach to anonymize the survey rating data that scales almost linearly with the input size, and we apply the method to two real-life data sets to demonstrate their efficiency and practical utility. |
Keywords | privacy; data anonymization; survey rating data |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
460499. Cybersecurity and privacy not elsewhere classified | |
460908. Information systems organisation and management | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Department of Mathematics and Computing |
Victoria University | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q12zq/on-the-identity-anonymization-of-high-dimensional-rating-data
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