Abstract
State-of-the-art sensitive information detection in unstructured data relies on the frequency of co-occurrence of keywords with sensitive seed words. In practice, however, this may fail to detect more complex patterns of sensitive information. In this work, we propose learning phrase structures that separate sensitive from non-sensitive documents in recursive neural networks. Our evaluation on real data with human labeled sensitive content shows that our new approach outperforms existing keyword based strategies.
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References
Berardi, G., Esuli, A., Macdonald, C., Ounis, I., Sebastiani, F.: Semi-automated text classification for sensitivity identification. In: CIKM, pp. 1711–1714 (2015)
Chow, R., Philippe, G., Staddon, J.: Detecting privacy leaks using corpus-based association rules. In: ACM SIGKDD, pp. 893–901 (2008)
Cormack, G.V., Grossman, M.R., Hedin, B., Oard, D.W.: Overview of the TREC 2010 legal track. In: TREC (2010)
Goller, C., Kuchler, A.: Learning task-dependent distributed representations by backpropagation through structure. In: IEEE ICNN, pp. 347–352 (1996)
Grechanik, M., McMillan, C., Dasgupta, T., Poshyvanyk, D., Gethers, M.: Redacting sensitive information in software artifacts. In: ICPC, pp. 314–325 (2014)
Hart, M., Manadhata, P., Johnson, R.: Text classification for data loss prevention. In: Fischer-Hübner, S., Hopper, N. (eds.) PETS 2011. LNCS, vol. 6794, pp. 18–37. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22263-4_2
Irsoy, O., Cardie, C.: Deep recursive neural networks for compositionality in language. In: NIPS, pp. 2096–2104 (2014)
Klimt, B., Yang, Y.: The enron corpus: a new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_22
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
Sánchez, D., Batet, M.: C-sanitized: a privacy model for document redaction and sanitization. JASIST 67, 148–163 (2016)
Socher, R., Huang, E.H., Pennin, J., Manning, C.D., Ng, A.Y.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: NIPS (2011)
Socher, R., Lin, C.C., Manning, C., Ng, A.Y.: Parsing natural scenes and natural language with recursive neural networks. In: ICML, pp. 129–136 (2011)
Socher, R., Manning, C.D., Ng, A.Y.: Learning continuous phrase representations and syntactic parsing with recursive neural networks. In: NIPS WS Deep Learning and Unsupervised Feature Learning, pp. 1–9 (2010)
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP, pp. 1631–1642 (2013)
Taylor, A., Marcus, M., Santorini, B.: The Penn treebank: an overview. In: Abeillé, A. (ed.) Treebanks. Text, Speech and Language Technology, vol. 20. Springer, Dordrecht (2003). https://doi.org/10.1007/978-94-010-0201-1_1
Tomlinson, S.: Learning task experiments in the TREC 2010 legal track. In: TREC (2010)
Acknowledgments
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 645198 (Organicity Project) and No. 732240 (Synchronicity Project).
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Neerbek, J., Assent, I., Dolog, P. (2018). Detecting Complex Sensitive Information via Phrase Structure in Recursive Neural Networks. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_30
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