Behavior-based modeling and its application to Email analysis
ACM Transactions on Internet Technology (TOIT)
IT Professional
Data Leak Prevention through Named Entity Recognition
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
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Nowadays, the importance of corporations' business information is getting higher and industry people are trying to find software solution preventing the information asset from being disclosed by attackers. There are several representative commercial tools for this purpose and the tools are deployed in many corporations which are handling the critical information such as trade secret, intellectual property and personal information. The tools usually monitor traffic which can contain the important information and also they are watching the e-mail and instant messenger's content. In this work, we are considering the privacy violations in the procedures of data leakage prevention especially the monitoring procedures. In addition, we have tried to make a data model considering the trade-off relation between data leakage prevention and privacy violation. Specifically speaking, we have analyzed the information units of email and instant messenger and assigned a kind of assigned distinct weight values in the privacy and leakage protection viewpoints. In addition, we have shown a case how the weight values are accumulated to represent privacy violation level and data leakage prevention level. Our data model, weight value assignment result and the two kinds of level derivation process are implemented as a database model and user interface.