Abstract
Private information management and compliance are important issues nowadays for most of organizations. As a major communication tool for organizations, email is one of the many potential sources for privacy leaks. Information extraction methods have been applied to detect private information in text files. However, since email messages usually consist of low quality text, information extraction methods for private information detection may not achieve good performance. In this paper, we address the problem of predicting the presence of private information in email using data mining and text mining methods. Two prediction models are proposed. The first model is based on association rules that predict one type of private information based on other types of private information identified in emails. The second model is based on classification models that predict private information according to the content of the emails. Experiments on the Enron email dataset show promising results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Databases, Santiago, Chile, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Privacy-Preserving Data Mining. In: Proceedings of the ACM SIGMOD Conference on Management of Data, Dallas, Texas, pp. 439–450 (2000)
Armour, Q., Elazmeh, W., El-Kadri, N., Japkowicz, N., Matwin, S.: Privacy Compliance Enforcement in Email. In: Canadian Conference on AI, pp. 194–204 (2005)
Boufaden, N., Elazmeh, W., Ma, Y., Matwin, S., El-Kadri, N., Japkowicz, N.: PEEP - An Information Extraction base approach for Privacy Protection in Email. In: CEAS (2005)
Carvalho, V.R., Cohen, W.W.: Preventing Information Leaks in Email. In: SDM (2007)
Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy Preserving Mining of Association Rules. In: Proceedings of 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2002)
Han, H., Giles, C.L., Manavoglu, E., Zha, H., Zhang, Z., Fox, E.A.: Automatic Document Metadata Extraction Using Support Vector Machines. In: Proceedings of the 2003 Joint Conference o Digital Libraries (JDCL 2003), pp. 37–48 (2003)
Korba, L., Song, R., Yee, G., Patrick, A., Buffett, S., Wang, Y., Geng, L.: Private Data Management in Collaborative Environments. In: Luo, Y. (ed.) CDVE 2007. LNCS, vol. 4674, pp. 88–96. Springer, Heidelberg (2007)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Micheline Kamber Publishers (2006)
Jones, K.S., Willet, P.: Readings in Information Retrieval. Morgan Kaufmann, San Francisco (1997)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Geng, L., Korba, L., Wang, X., Wang, Y., Liu, H., You, Y. (2008). Using Data Mining Methods to Predict Personally Identifiable Information in Emails. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-88192-6_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88191-9
Online ISBN: 978-3-540-88192-6
eBook Packages: Computer ScienceComputer Science (R0)