On the Optimal Selection of k in the k-Anonymity Problem

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.