Abstract
In this paper we use information retrieval metrics to evaluate the effect of a document sanitization process, measuring information loss and risk of disclosure. In order to sanitize the documents we have developed a semi-automatic anonymization process following the guidelines of Executive Order 13526 (2009) of the US Administration. It embodies two main and independent steps: (i) identifying and anonymizing specific person names and data, and (ii) concept generalization based on WordNet categories, in order to identify words categorized as classified. Finally, we manually revise the text from a contextual point of view to eliminate complete sentences, paragraphs and sections, where necessary. For empirical tests, we use a subset of the Wikileaks Cables, made up of documents relating to five key news items which were revealed by the cables.
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References
Executive Order 13526, of the US Administration: Classified National Security Information, Section 1.4, points (a) to (h) (2009). http://www.whitehouse.gov/the-press-office/executive-order-classified-national-security-information
Wikileaks Cable repository. http://www.cablegatesearch.net
Chakaravarthy, V.T., Gupta, H., Roy, P., Mohania, M.K.: Efficient techniques for document sanitization. In: CIKM 2008, Napa Valley, California, USA, October 26–30 (2008)
Saygin, Y., Hakkani-Tr, D., Tur, G.: Sanitization and Anonymization of Document Repositories (2009)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. (IJUFKS) 10(5), 557–570 (2002)
Cumby, C., Ghani, R.: A machine learning based system for semi-automatically redacting documents. In: Proceedings of IAAI 2011 (2011)
Hong, T.-P., Lin, C.-W., Yang, K.-T., Wang, S.-L.: A heuristic data-sanitization approach based on TF-IDF. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011, Part I. LNCS, vol. 6703, pp. 156–164. Springer, Heidelberg (2011)
Samelin, K., Pöhls, H.C., Bilzhause, A., Posegga, J., de Meer, H.: Redactable signatures for independent removal of structure and content. In: Ryan, M.D., Smyth, B., Wang, G. (eds.) ISPEC 2012. LNCS, vol. 7232, pp. 17–33. Springer, Heidelberg (2012)
Chow, R., Staddon, J.N., Oberst, I.S.: Method and apparatus for facilitating document sanitization. US Patent Application Pub. No. US 2011/0107205 A1, May 5 (2011)
Neamatullah, I., Douglass, M.M., Lehman, L.H., Reisner, A., Villarroel, M., Long, W.J., Szolovits, P., Moody, G.B., Mark, R.G., Clifford, G.D.: Automated de-identification of free-text medical records. BMC Med. Inf. Decis. Making 8, 32 (2008)
Anandan, B., Clifton, C., Jiang, W., Murugesan, M., Pastrana-Camacho, P., Si, L.: t-Plausibility: generalizing words to desensitize text. Trans. Data Priv. 5(3), 505–534 (2012)
Miller, G.A., Beckwith, R., Fellbaum, C.D., Gross, D., Miller, K.: WordNet: an online lexical database. Int. J. Lexicograph 3(4), 235–244 (1990)
Pingar: Entity extraction software. http://www.pingar.com
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval: The Concepts and Technology Behind Search, 2nd edn. ACM Press Books, England (2011)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Yahoo! News. Top 10 revelations from Wiki Leaks cables. http://news.yahoo.com/blogs/lookout/top-10-revelations-wikileaks-cables.html
Abril, D., Navarro-Arribas, G., Torra, V.: On the Declassification of Confidential Documents: Modeling Decision for Artificial Intelligence. Springer, Berlin (2011)
Acknowledgments
This research is partially supported by the Spanish MEC projects CONSOLIDER INGENIO 2010 CSD2007-00004 and eAEGIS TSI2007-65406-C03-02. The work contributed by the second author was carried out as part of the Computer Science Ph.D. program of the Universitat Autònoma de Barcelona (UAB).
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Nettleton, D.F., Abril, D. (2015). An Information Retrieval Approach to Document Sanitization. In: Navarro-Arribas, G., Torra, V. (eds) Advanced Research in Data Privacy. Studies in Computational Intelligence, vol 567. Springer, Cham. https://doi.org/10.1007/978-3-319-09885-2_9
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DOI: https://doi.org/10.1007/978-3-319-09885-2_9
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