# Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation

arXiv: https://arxiv.org/abs/2101.07365 · HTML: https://mlsanity.com/paper/2101.07365/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 68 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Gazelle: A Low Latency Framework for Secure Neural Network Inference](https://mlsanity.com/paper/1801.05507/) (2018)
- [CrypTFlow2: Practical 2-Party Secure Inference](https://mlsanity.com/paper/2010.06457/) (2020)
- [Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications](https://mlsanity.com/paper/1801.03239/) (2018)
- [CrypTFlow: Secure TensorFlow Inference](https://mlsanity.com/paper/1909.07814/) (2019)
- [XONN: XNOR-based Oblivious Deep Neural Network Inference](https://mlsanity.com/paper/1902.07342/) (2019)
- [QUOTIENT: Two-Party Secure Neural Network Training and Prediction](https://mlsanity.com/paper/1907.03372/) (2019)
- [Secure Evaluation of Quantized Neural Networks](https://mlsanity.com/paper/1910.12435/) (2019)
- [Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs](https://mlsanity.com/paper/1803.04337/) (2018)
- [Protecting Privacy of Users in Brain-Computer Interface Applications](https://mlsanity.com/paper/1907.01586/) (2019)
- [PrivFT: Private and Fast Text Classification with Homomorphic Encryption](https://mlsanity.com/paper/1908.06972/) (2019)
- [High Performance Logistic Regression for Privacy-Preserving Genome Analysis](https://mlsanity.com/paper/2002.05377/) (2020)
- [Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detection](https://mlsanity.com/paper/1906.02325/) (2019)
- [Weakening the Isolation Assumption of Tamper-proof Hardware Tokens](https://mlsanity.com/paper/1502.03487/) (2015)
- [A Framework for Efficient Adaptively Secure Composable Oblivious Transfer in the ROM](https://mlsanity.com/paper/1710.08256/) (2017)

## Built on by (most cited first, 2 of 2)

- [Privacy-Preserving Collaborative Learning through Feature Extraction](https://mlsanity.com/paper/2212.06322/) (2022)
- [Round and Communication Balanced Protocols for Oblivious Evaluation of Finite State Machines](https://mlsanity.com/paper/2103.11240/) (2021)

## Abstract (arXiv)

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.