By Daniele Perito, Claude Castelluccia, Mohamed Ali Kaafar (auth.), Simone Fischer-Hübner, Nicholas Hopper (eds.)
This ebook constitutes the refereed court cases of the tenth foreign Symposium, PETS 2011, held in Waterloo, Canada, in July 2011.
The 15 revised complete papers have been conscientiously reviewed and chosen from sixty one submissions. The papers deal with layout and awareness of privateness providers for the web, different information structures and communique networks. proposing novel examine on all theoretical and sensible facets of privateness applied sciences, in addition to experimental reports of fielded platforms the amount additionally gains novel technical contributions from different groups akin to legislation, company, and knowledge safeguard specialists, that current their views on technological issues.
Read Online or Download Privacy Enhancing Technologies: 11th International Symposium, PETS 2011, Waterloo, ON, Canada, July 27-29, 2011. Proceedings PDF
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Additional info for Privacy Enhancing Technologies: 11th International Symposium, PETS 2011, Waterloo, ON, Canada, July 27-29, 2011. Proceedings
Sample text
Johnson we focus on recognizing sensitive information that either it has either seen before or is similar to an existing confidential document. In the future, we hope to explore how the system can infer if a document is sensitive if it has zero training data to support this decision (possibly relying on metadata). Our study demonstrates that DLP systems face an inherent class imbalance issue: nearly all documents that exist are outside the organization and are not sensitive. To train a classifier on this class is simply infeasible because of its size.
Private enterprise data (secret ) may include internal policy manuals, legal agreements, financial records, private customer data, source code or other trade secrets. Non-enterprise data (NE ) is everything else, and so cannot be described succinctly, but is likely to include personal emails, Facebook pages, news articles, and web pages from other organizations, some of which may be topically related to the business of the enterprise. We consider private documents to be confidential and require protection whereas N E and public documents do not.
157–164. IEEE Computer Society Press, Los Alamitos (2007) 40. : Feature selection for unbalanced class distribution and naive bayes. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML 1999, pp. 258–267. Morgan Kaufmann, San Francisco (1999) 41. : A comparative study of the practical characteristics of neural network and conventional pattern classifiers. Technical report (1990) 42. Church of Jesus Christ of Latter Day Saints. org 43. : Learning to classify short and sparse text & web with hidden topics from large-scale data collections.