Privacy Preserving Spam Filtering

TL;DR

This paper proposes a privacy-preserving spam filtering system that allows training and evaluating classifiers on encrypted email data without exposing individual emails, using cryptographic primitives and dimensionality reduction techniques.

Contribution

It introduces a novel protocol combining homomorphic encryption and randomization for privacy-preserving spam classification, and demonstrates its practicality with large-scale experiments.

Findings

Achieves high accuracy with privacy-preserving protocols.

Reduces computational complexity via data-independent dimensionality reduction.

Demonstrates feasibility on large-scale spam filtering tasks.

Abstract

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.