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Privacy-Aware Collaborative Spam Filtering
May 2009 (vol. 20 no. 5)
pp. 725-739
ASCII Text
x 
 
Kang Li, Zhenyu Zhong, Lakshmish Ramaswamy, "Privacy-Aware Collaborative Spam Filtering," IEEE Transactions on Parallel and Distributed Systems, vol. 20, no. 5, pp. 725-739, May, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TPDS.2008.143,
author = {Kang Li and Zhenyu Zhong and Lakshmish Ramaswamy},
title = {Privacy-Aware Collaborative Spam Filtering},
journal ={IEEE Transactions on Parallel and Distributed Systems},
volume = {20},
number = {5},
issn = {1045-9219},
year = {2009},
pages = {725-739},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2008.143},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Parallel and Distributed Systems
TI - Privacy-Aware Collaborative Spam Filtering
IS - 5
SN - 1045-9219
SP725
EP739
EPD - 725-739
A1 - Kang Li,
A1 - Zhenyu Zhong,
A1 - Lakshmish Ramaswamy,
PY - 2009
KW - Distributed systems
KW - collaboration
KW - spam
KW - privacy.
VL - 20
JA - IEEE Transactions on Parallel and Distributed Systems
ER -
 
Kang Li, University of Georgia, Athens
Zhenyu Zhong, Secure Computing, Alpharetta
Lakshmish Ramaswamy, University of Georgia, Athens
While the concept of collaboration provides a natural defense against massive spam e-mails directed at large numbers of recipients, designing effective collaborative anti-spam systems raises several important research challenges. First and foremost, since e-mails may contain confidential information, any collaborative anti-spam approach has to guarantee strong privacy protection to the participating entities. Second, the continuously evolving nature of spam demands the collaborative techniques to be resilient to various kinds of camouflage attacks. Third, the collaboration has to be lightweight, efficient, and scalable. Toward addressing these challenges, this paper presents ALPACAS—a privacy-aware framework for collaborative spam filtering. In designing the ALPACAS framework, we make two unique contributions. The first is a feature-preserving message transformation technique that is highly resilient against the latest kinds of spam attacks. The second is a privacy-preserving protocol that provides enhanced privacy guarantees to the participating entities. Our experimental results conducted on a real e-mail data set shows that the proposed framework provides a 10 fold improvement in the false negative rate over the Bayesian-based Bogofilter when faced with one of the recent kinds of spam attacks. Further, the privacy breaches are extremely rare. This demonstrates the strong privacy protection provided by the ALPACAS system.

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Index Terms:
Distributed systems, collaboration, spam, privacy.
Citation:
Kang Li, Zhenyu Zhong, Lakshmish Ramaswamy, "Privacy-Aware Collaborative Spam Filtering," IEEE Transactions on Parallel and Distributed Systems, vol. 20, no. 5, pp. 725-739, May 2009, doi:10.1109/TPDS.2008.143
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