Title :
Predicting Sensitive Relationships from Email Corpus
Author_Institution :
Sch. of Comput. Sci., Sichuan Univ., Chengdu, China
Abstract :
In this paper, we focus on the problem of predicting sensitive relationships from Email corpus. We refer to the problem of predicting sensitive relationships from a social network as link re-identification. We propose a predicting sensitive relationships method which has two steps. First step is counting mutual privacy communication. Second step is evaluation cluster factor. Experimental results on Enron email corpus are presented to support our analysis.
Keywords :
data privacy; directed graphs; electronic mail; social networking (online); Enron email corpus; directed weighted graph; graph structure; link re-identification; mutual privacy communication; sensitive relationship prediction; social network; Electronic mail; Organizational aspects; Organizations; Prediction algorithms; Privacy; Social network services; Strontium; email corpus; predict; sensitive relationships; social network;
Conference_Titel :
Genetic and Evolutionary Computing (ICGEC), 2010 Fourth International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4244-8891-9
Electronic_ISBN :
978-0-7695-4281-2
DOI :
10.1109/ICGEC.2010.72
