Private Text Classification with Convolutional Neural Networks
Text classifiers are regularly applied to personal texts, leaving users of these classifiers vulnerable to privacy breaches. We propose a solution for privacy-preserving text classification that is based on Convolutional Neural Networks (CNNs) and Secure Multiparty Computation (MPC). Our method enables the inference of a class label for a personal text in such a way that (1) the owner of the personal text does not have to disclose their text to anyone in an unencrypted manner, and (2) the owner of the text classifier does not have to reveal the trained model parameters to the text owner or to anyone else. To demonstrate the feasibility of our protocol for practical private text classification, we implemented it in the PyTorch-based MPC framework CrypTen, using a well-known additive secret sharing scheme in the honest-but-curious setting. We test the runtime of our privacy-preserving text classifier, which is fast enough to be used in practice.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationPrivacy Preservingtext-classificationText ClassificationSimilar Papers 제목 키워드 기반
Privacy-Preserving Video Classification with Convolutional Neural Networks
Many video classification applications require access to personal data, thereby posing an invasive security risk to the users' privacy. We propose a privacy-preserving implementation of single-frame method based video cl…
ClassificationEmotion RecognitionGeneral Classificationimage-classification+3Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
Convolutional neural networks excel in histopathological image classification, yet their pixel-level focus hampers explainability. Conversely, emerging graph convolutional networks spotlight cell-level features and medic…
ClassificationHistopathological Image Classificationimage-classificationImage ClassificationScalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy
Large convolutional neural networks (CNN) can be difficult to train in the differentially private (DP) regime, since the optimization algorithms require a computationally expensive operation, known as the per-sample grad…
Differentially Private Synthetic Medical Data Generation using Convolutional GANs
Deep learning models have demonstrated superior performance in several application problems, such as image classification and speech processing. However, creating a deep learning model using health record data requires a…
Deep Learningimage-classificationImage ClassificationSynthetic Data GenerationPrivacy-Preserving Graph Convolutional Networks for Text Classification
Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e.g., citation or social networks. However, sensitive personal information, such as…
ClassificationGeneral ClassificationPrivacy PreservingRepresentation Learning+2