By Sylvia Mercado Kierkegaard (ed.)
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Extra info for Cyberlaw, Security and Privacy
As expected, real email data have several inconsistencies. To help mitigate some of these problems, we used the Enron dataset version compiled by Shetty and Adibi [Shetty and Adibi, 2004], in which a large number of repeated messages were removed. This version contains 252,759 messages from 151 employees distributed in approximately 3000 folders. Another particularly important type of inconsistency in the corpus is the fact that a single user may have multiple email addresses. 2 The Enron Dataset 37 email address for some email users.
A|A| , we find N(q): its K most similar messages (neighbors) in the training set. The notion of similarity here is also defined as the cosine distance between the text of two normalized TFIDF vectors. With the top K most similar messages selected from the training set, we then computed the weight of each recipient ai according to the sum of similarity scores of the neighboring messages in which ai was one of the recipients. After ranking all |A| recipients in the given message according to this method, we selected the one with lowest score as the predicted leak-recipient.
Person] could [person] [person] please ? thanks are [person] can [person] need to 3-gram [person] need to [wwhh] do [person] let [me] know would [person] do [person] think are [person] meeting could [person] please do [person] need 4-gram [wwhh] do [person] think do [person] need to and let [me] know call [number]-[number] would be able to [person] think [person] need let [me] know [wwhh] do [person] think ? 5-gram [wwhh] do [person] think ? 10 Request Act:Top eight N-grams Selected by Information Gain.
Cyberlaw, Security and Privacy by Sylvia Mercado Kierkegaard (ed.)