Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation
Authors: Devin Reich, Ariel Todoki, Rafael Dowsley, Martine De Cock, anderson nascimento
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform end-to-end experiments with an application for detecting hate speech against women and immigrants, demonstrating excellent runtime results without loss of accuracy. We evaluate the proposed protocols in a use case for the detection of hate speech in short text messages, using data from [6]. |
| Researcher Affiliation | Academia | Devin Reich1, Ariel Todoki1, Rafael Dowsley2, Martine De Cock1 , Anderson Nascimento1 1 School of Engineering and Technology University of Washington Tacoma Tacoma, WA 98402 EMAIL 2Department of Computer Science Bar-Ilan University, 5290002, Ramat-Gan, Israel EMAIL |
| Pseudocode | Yes | The paper presents structured steps for protocols such as 'Protocol EQ', 'Protocol FE', 'Protocol AB', 'Protocol TC LR', and 'Protocol TC AB'. For example, 'Protocol AB: Alice and Bob hold secret sharings [[xi]]q of each of the n binary features xi. Bob holds the trained Ada Boost model which consists of two weighted probability vectors y...' |
| Open Source Code | Yes | We build our protocols using a privacy-preserving machine learning (PPML) framework based on SMC developed by us3. 3https://bitbucket.org/uwtppml |
| Open Datasets | Yes | We evaluate the proposed protocols in a use case for the detection of hate speech in short text messages, using data from [6]. [6] Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Rangel, Paolo Rosso, and Manuela Sanguinetti. Semeval-2019 Task 5: Multilingual detection of hate speech against immigrants and women in Twitter. |
| Dataset Splits | Yes | The models are evaluated using 5-fold cross-validation over the entire corpus of 10,000 tweets. |
| Hardware Specification | Yes | We implemented the protocols from Section 3 in Java and ran experiments on AWS c5.9xlarge machines with 36 v CPUs, 72.0 Gi B Memory. |
| Software Dependencies | No | The paper states, 'We implemented the protocols from Section 3 in Java,' but does not specify a Java version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The models are evaluated using 5-fold cross-validation over the entire corpus of 10,000 tweets. Each runtime experiment was repeated 3 times and average results are reported. Table 1 shows accuracy results for a variety of models trained to classify a tweet as hate speech vs. non-hate speech, including tree ensemble models consisting of 50, 200, and 500 decision stumps, and LR models trained on 50, 200, 500, and all features. |