BlindFilter: Privacy-Preserving Spam Email Detection Using Homomorphic Encryption

Dongwon Lee, Myeonghwan Ahn, Hyesun Kwak, Jin B. Hong, Hyoungshick Kim

Abstract: Spam filtering services typically operate via cloud outsourcing, which exposes sensitive and private email content to the cloud server spam filter. Homomorphic encryption (HE) can address this issue by ensuring that user emails remain encrypted throughout all stages of the spam detection process on the cloud server. However, existing HE-based approaches are computationally infeasible due to the nature of HE operations. This paper proposes BlindFilter, a distributed, lightweight, HE-based spam email detection approach that consists of clients and servers collaborating to perform spam detection operations securely. BlindFilter employs WordPiece encoding and a modified Naive Bayes classifier, mitigating the need for multiplications and comparisons that would be prohibitive in terms of computation when applied with HE. Our experimental results demonstrate the efficacy of BlindFilter, with F1 scores exceeding 97% across two public email datasets. Furthermore, BlindFilter proves to be efficient as it can process an email in an average of 482.78 milliseconds. Our analysis also reveals that BlindFilter is robust against model extraction attacks, in which malicious users attempt to deduce the features of BlindFilter from query-response pairs.