Abstract
Enterprises constantly share and exchange digital documents with sensitive information both within the organization and with external partners/customers. With the increase in digital data sharing, data breaches have also increased significantly resulting in sensitive information being accessed by unintended recipients. To protect documents against such unauthorized access, the documents are assigned a security policy which is a set of users and information about their access permissions on the document. With the surge in the volume of digital documents, manual assignment of security policies is infeasible and error prone calling for an automatic policy assignment. In this paper, we propose an algorithm that analyzes the sensitive information and historic access permissions to identify content-access correspondence via a novel multi-label classifier formulation. The classifier thus modeled is capable of recommending policies/access permissions for any new document. Comparisons with existing approaches in this space shows superior performance with the proposed framework across several evaluation criteria.
Keywords
- Digital Rights Management
- Extreme Multi-label Learning
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Enron email dataset webpage. http://www.cs.cmu.edu/~enron/
The history of data breaches. https://digitalguardian.com/blog/history-data-breaches. Accessed 22 Oct 2016
Stanford Named Entity Recognizer (NER). http://nlp.stanford.edu/software/CRF-NER.shtml. Accessed 26 Oct 2016
Wikileaks cablegate data. https://wikileaks.org/plusd/
Agrawal, R., Gupta, A., Prabhu, Y., Varma, M.: Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 13–24. ACM (2013)
Aura, T., Kuhn, T.A., Roe, M.: Scanning electronic documents for personally identifiable information. In: Proceedings of the 5th ACM Workshop on Privacy in Electronic Society, pp. 41–50. ACM (2006)
Carvalho, V.R., Cohen, W.W.: Preventing information leaks in email. In: International Conference on Data Mining (SDM). SIAM (2007)
Carvalho, V.R., Cohen, W.W.: Ranking users for intelligent message addressing. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 321–333. Springer, Heidelberg (2008). doi:10.1007/978-3-540-78646-7_30
Cumby, C.M., Ghani, R.: A machine learning based system for semi-automatically redacting documents. In: IAAI (2011)
Gabrilovich, E., Markovitch, S.: Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4. 5. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 41. ACM (2004)
Geng, L., Korba, L., Wang, X., Wang, Y., Liu, H., You, Y.: 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.) ADMA 2008. LNCS (LNAI), vol. 5139, pp. 272–281. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88192-6_26
Graus, D., Van Dijk, D., Tsagkias, M., Weerkamp, W., De Rijke, M.: Recipient recommendation in enterprises using communication graphs and email content. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1079–1082. ACM (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). doi:10.1007/978-3-642-22263-4_2
Johnson, M.E.: Information risk of inadvertent disclosure: an analysis of file-sharing risk in the financial supply chain. J. Manag. Inf. Syst. 25(2), 97–124 (2008)
Leopold, E., Kindermann, J.: Text categorization with support vector machines. How to represent texts in input space? Mach. Learn. 46(1–3), 423–444 (2002)
Liu, T., Pu, Y., Shi, J., Li, Q., Chen, X.: Towards misdirected email detection for preventing information leakage. In: 2014 IEEE Symposium on Computers and Communications (ISCC), pp. 1–6. IEEE (2014)
Prabhu, Y., Varma, M.: FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 263–272. ACM (2014)
Sorower, M.S.: A Literature Survey on Algorithms for Multi-label Learning. Oregon State University, Corvallis (2010)
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Verizon RISK Team: 2014 data breach investigations report (2014)
Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406–417. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74958-5_38
Zilberman, P., Dolev, S., Katz, G., Elovici, Y., Shabtai, A.: Analyzing group communication for preventing data leakage via email. In: 2011 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 37–41. IEEE (2011)
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Goyal, T., Mehta, S., Srinivasan, B.V. (2017). Preventing Inadvertent Information Disclosures via Automatic Security Policies. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_14
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DOI: https://doi.org/10.1007/978-3-319-57454-7_14
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