A new anti-spam model based on e-mail address concealment technique

To deal with the junk e-mail problem caused by the e-mail address leakage for a majority of Internet users, this paper presents a new privacy protection model in which the e-mail address of the user is treated as a piece of privacy information concealed. Through an interaction pattern that involves three parties and uses an e-mail address code in the place of an e-mail address, the proposed model can prevent the e-mail address from being leaked, thus effectively resolving the junk e-mail problem. We compare the proposed anti-spam method with the filtering technology based on machine learning. The result shows that 100% spams can be filtered out in our scheme, indicating the effectiveness of the proposed anti-spam method.

[1]  Jingsha He,et al.  Privacy Reference Monitor - A Computer Model for Law Compliant Privacy Protection , 2009, 2009 15th International Conference on Parallel and Distributed Systems.

[2]  Klemens Böhm,et al.  Understanding User Preferences and Awareness: Privacy Mechanisms in Location-Based Services , 2009, OTM Conferences.

[3]  Tsuhan Chen,et al.  A collaborative anti-spam system , 2009, Expert Syst. Appl..

[4]  Yiqun Liu,et al.  Identifying web spam with user behavior analysis , 2008, AIRWeb '08.

[5]  George A. Vouros,et al.  Discovering Subsumption Hierarchies of Ontology Concepts from Text Corpora , 2007 .

[6]  Asim Karim,et al.  PSSF: A Novel Statistical Approach for Personalized Service-side Spam Filtering , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[7]  Gondy Leroy,et al.  A decision support system: Automated crime report analysis and classification for e-government , 2014, Gov. Inf. Q..

[8]  RAJENDRA KUMAR ROUL,et al.  Detecting spam web pages using content and link-based techniques , 2016 .

[9]  Kang Li,et al.  Privacy-Aware Collaborative Spam Filtering , 2009, IEEE Transactions on Parallel and Distributed Systems.

[10]  Fayez Gebali,et al.  A spam rejection scheme during SMTP sessions based on layer-3 e-mail classification , 2009, J. Netw. Comput. Appl..

[11]  A. B. M. Shawkat Ali,et al.  Spam Classification Using Adaptive Boosting Algorithm , 2007, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007).

[12]  Yi Xiu-shuang A Collaborative Anti-spam System Based on First-Second Filtering , 2007 .

[13]  M. Basavaraju,et al.  A Novel Method of Spam Mail Detection using Text Based Clustering Approach , 2010 .