Filtering spam depending on your reputation (on the amount of spam you typically receive)

Abaca has recently proposed an effective way of filtering spam emails. It is called receiver reputation.

It relies on this fact
One can group receivers by the amount of spam they receive on a daily basis. Say we consider 5 groups. “People in Group 1 receive, on average, 90% spam. Group 2 receives 70% spam, Group 3 receives 50% spam, Group 4 receives 30% spam, and Group 5 receives 10% spam.”

How it works
Messages are classified whether they are spam or not depending on the receiver of the message, “rather than where the message is FROM or what it CONTAINS“. “Essentially, if the message is sent to users who typically receive a high percentage of spam, the message is more likely to be spam. However, if the message is sent to users who typically receive a low percentage of spam, the message is more likely to be legitimate. Combining the reputations of all recipients of a particular message, therefore, is equivalent to combining those users’ rating power to estimate the legitimacy of the sender and the message”

What about new users?
“The system can be bootstrapped from an empty database with just 2 users (someone who gets a lot of spam and someone who gets a lot of ham). … The system was initially seeded with just two users: a person who receives virtually all spam and a person who receives virtually all legitimate mail. The statistics of a third user was then approximated using the ratings established by the first two users. The fourth user was added with that user’s statistics approximated by the first three users, etc.”
“The amazing thing is no human is required to read or rate any email; the system gets smarter on it’s own without any human intervention”

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