  Traditional spam classification requires the end-user to reveal the content
of its received email to the spam classifier which violates the privacy. Spam
classification over encrypted emails enables the classifier to classify spam
email without accessing the email, hence protects the privacy of email content.
In this paper, we construct a spam classification framework that enables the
classification of encrypted emails. Our classification model is based on a
neural network with a quadratic network part and a multi-layer perception
network part. The quadratic network architecture is compatible with the
operation of an existing quadratic functional encryption scheme that enables
our classification to predict the label of encrypted emails without revealing
the associated plain-text email. The evaluation results on real-world spam
datasets indicate that our proposed spam classification model achieves an
accuracy of over 96%.
