On the comparison of microdata disclosure control algorithms
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
POkA: identifying pareto-optimal k-anonymous nodes in a domain hierarchy lattice
Proceedings of the 18th ACM conference on Information and knowledge management
On the identification of property based generalizations in microdata anonymization
DBSec'10 Proceedings of the 24th annual IFIP WG 11.3 working conference on Data and applications security and privacy
Efficient discovery of de-identification policy options through a risk-utility frontier
Proceedings of the third ACM conference on Data and application security and privacy
Journal of Computer Security
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When disseminating data involving human subjects, researchers have to weigh in the requirements of privacy of the individuals involved in the data. A model widely used for enhancing individual privacy is k-anonymity, where an individual data record is rendered similar to k - 1 other records in the data set by using generalization and/or suppression operations on the data attributes. The drawback of this model is that such transformations result in considerable loss of information that is proportional to the choice of k. Studies in this context have so far focused on minimizing the information loss for some given value of k. However, owing to the presence of outliers, a specified k value may or may not be obtainable. Further, an exhaustive analysis is required to determine a k value that fits the loss constraint specified by a data publisher. In this paper, we formulate a multi-objective optimization problem to illustrate that the decision on k can be much more informed than being a choice solely based on the privacy requirement. The optimization problem is intended to resolve the issue of data privacy when data suppression is not allowed in order to obtain a particular value of k. An evolutionary algorithm is employed here to provide this insight.