  Email is a private medium of communication, and the inherent privacy
constraints form a major obstacle in developing effective spam filtering
methods which require access to a large amount of email data belonging to
multiple users. To mitigate this problem, we envision a privacy preserving spam
filtering system, where the server is able to train and evaluate a logistic
regression based spam classifier on the combined email data of all users
without being able to observe any emails using primitives such as homomorphic
encryption and randomization. We analyze the protocols for correctness and
security, and perform experiments of a prototype system on a large scale spam
filtering task.
  State of the art spam filters often use character n-grams as features which
result in large sparse data representation, which is not feasible to be used
directly with our training and evaluation protocols. We explore various data
independent dimensionality reduction which decrease the running time of the
protocol making it feasible to use in practice while achieving high accuracy.
