Abstract-Data publishing can provide enormous benefits to the society. However, due to privacy concerns, data cannot be published in their original forms. Two types of data publishing can address the privacy issue: one is to publish the sanitized version of the original data, and the other is to publish the aggregate information from the original data, such as data mining results. There have been extensive studies to understand the privacy consequence in the first approach, but there is not much investigation on the privacy consequence of publishing data mining results, although, it is well believed that publishing data mining results can lead to the disclosure of private information. We propose a systematic method to study the privacy consequence of data mining results. Based on a well-established theory, the principle of maximum entropy, we have developed a method to precisely quantify the privacy risk when data mining results are published. We take the association rule mining as an example in this paper, and demonstrate how we quantify the privacy risk based on the published association rules. We have conducted experiments to evaluate the effectiveness and performance of our method. We have drawn several interesting observations from our experiments.
Instead of publishing an anonymized table, REF consider publishing the data mining results in the form of association rules that can be obtained from the original table.
Deriving Private Information from Association Rule Mining Results
2009 IEEE 25th International Conference on Data Engineering
2009 IEEE 25th International Conference on Data Engineering
Computer Science