Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Proceedings of the 16th international conference on World Wide Web
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Anonymizing transaction databases for publication
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Publishing Sensitive Transactions for Itemset Utility
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
On the Anonymization of Sparse High-Dimensional Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Injecting purpose and trust into data anonymisation
Proceedings of the 18th ACM conference on Information and knowledge management
On the identity anonymization of high-dimensional rating data
Concurrency and Computation: Practice & Experience
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In this paper, we study a problem of privacy protection in large survey rating data. The rating data usually contains both ratings of sensitive and non-sensitive issues, and the ratings of sensitive issues include personal information. Even when survey participants do not reveal any of their ratings, their survey records are potentially identifiable by using information from other public sources. We propose a new (k,ε,l)-anonymity model, in which each record is required to be similar with at least k−1 others based on the non-sensitive ratings, where the similarity is controlled by ε, and the standard deviation of sensitive ratings is at least l. We study an interesting yet nontrivial satisfaction problem of the (k,ε,l)-anonymity, which is to decide whether a survey rating data set satisfies the privacy requirements given by users. We develop a slice technique for the satisfaction problem and the experimental results show that the slicing technique is fast, scalable and much more efficient in terms of execution time than the heuristic pairwise method.