June 19, 2021


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Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation. (arXiv:2101.07365v2 [cs.CR] UPDATED)

We propose a privacy-preserving Naive Bayes classifier and apply it to the
problem of private text classification. In this setting, a party (Alice) holds
a text message, while another party (Bob) holds a classifier. At the end of the
protocol, Alice will only learn the result of the classifier applied to her
text input and Bob learns nothing. Our solution is based on Secure Multiparty
Computation (SMC). Our Rust implementation provides a fast and secure solution
for the classification of unstructured text. Applying our solution to the case
of spam detection (the solution is generic, and can be used in any other
scenario in which the Naive Bayes classifier can be employed), we can classify
an SMS as spam or ham in less than 340ms in the case where the dictionary size
of Bob’s model includes all words (n = 5200) and Alice’s SMS has at most m =
160 unigrams. In the case with n = 369 and m = 8 (the average of a spam SMS in
the database), our solution takes only 21ms.