ALPACAS: A Large-Scale Privacy-Aware Collaborative Anti-Spam System

Abstract

While the concept of collaboration provides a natural defense against massive spam emails directed at large numbers of recipients, designing effective collaborative anti-spam systems raises several important research challenges. First and foremost, since emails 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. Towards addressing these challenges, this paper presents ALPACAS-a privacyaware 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 email dataset 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.

SocialFilter: Introducing social trust to collaborative spam mitigation

“…Prior work also includes proposals for collaborative spam filtering [3,7,29,30]. Kong et al [16] also consider untrustworthy reporters, using Eigentrust to derive their reputation.…”

“…Motivated by the shortcomings in terms of effectiveness and cost of email reputation services, researchers have proposed open and collaborative peer-to-peer spam filtering platforms, e.g., [29,30]. These collaborative systems assume compliant behavior from all participating spam reporting nodes, i.e., that nodes submit truthful reports regarding spammers.…”

Privacy-Aware Collaborative Spam Filtering

“…The authors would like to thank the anonymous reviewers for their insightful comments. A shorter version of this paper appeared in the Proceedings of the 27th Conference on Computer Communications (IEEE INFOCOM 2008) [1].…”

Symbiotic Data Mining for Personalized Spam Filtering

“…Yet, due to logistic and privacy issues, it is quite difficult to obtain such data and make it public. Hence, we will use a synthetic mixture of real spam and ham messages, in a strategy similar to what has been proposed in [6], [13]. The ham messages will be related to five Enron employees with the largest mailboxes collected during the same time period.…”

“…common spam words). CF can be based on blacklists [5], which contain IP addresses of known spam senders, or fingerprints extracted from spam messages [6]. Current research on spam CBF relies mainly on improving individual classifier performance, by a better preprocessing [4] or enhancement of the learning algorithm [7].…”