Privacy-Preserving Spam Filtering using Functional Encryption

• URL: http://arxiv.org/abs/2012.04163v1
• Date: Tue, 8 Dec 2020 02:14:28 GMT
• Title: Privacy-Preserving Spam Filtering using Functional Encryption
• Authors: Sicong Wang, Naveen Karunanayake, Tham Nguyen, Suranga Seneviratne
• Abstract summary: We construct a spam classification framework that enables the classification of encrypted emails. Our model is based on a neural network with a quadratic network part and a multi-layer perception network part. The evaluation results on real-world spam datasets indicate that our proposed spam classification model achieves an accuracy of over 96%.
• Score: 1.0019926246026924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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