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Agrawal, Nitin; Shamsabadi, Ali Shahin; Kusner, Matt J.; Gascón, Adrià

doi:10.1145/3319535.3339819

Cited by 138 publications

(34 citation statements)

References 24 publications

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“…The MPC protocols for text classification with CNNs that we use in this paper, are very similar to existing MPC protocols for image classification with CNNs (Agrawal et al, 2019;Dalskov et al, 2020;Juvekar et al, 2018;Kumar et al, 2020;Mishra et al, 2020). The main distinguishing aspect is that image classification is based on 2-dimensional (2D) CNNs, while for text classification it is common to use 1-dimensional (1D) CNNs in which the filters move in only one direction.…”

Section: Introductionmentioning

confidence: 73%

Supervised Morphological Segmentation Using Rich Annotated Lexicon

Ansari

Žabokrtský²,

Mahmoudi

et al. 2019

Proceedings - Natural Language Processing in a Deep Learning World

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Morphological segmentation of words is the process of dividing a word into smaller units called morphemes; it is tricky especially when a morphologically rich or polysynthetic language is under question. In this work, we designed and evaluated several Recurrent Neural Network (RNN) based models as well as various other machine learning based approaches for the morphological segmentation task. We trained our models using annotated segmentation lexicons. To evaluate the effect of the training data size on our models, we decided to create a large hand-annotated morphologically segmented corpus of Persian words, which is, to the best of our knowledge, the first and the only segmentation lexicon for the Persian language. In the experimental phase, using the hand-annotated Persian lexicon and two smaller similar lexicons for Czech and Finnish languages, we evaluated the effect of the training data size, different hyperparameters settings as well as different RNN-based models.

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Section: Introductionmentioning

confidence: 73%

Supervised Morphological Segmentation Using Rich Annotated Lexicon

Ansari

Žabokrtský²,

Mahmoudi

et al. 2019

Proceedings - Natural Language Processing in a Deep Learning World

View full text Add to dashboard Cite

show abstract

“…Still, it uses a square function to approximate the nonlinear activation function, which results in lower accuracy. The work of Agrawal et al 30 improves the accuracy by introducing Boolean operation, but it uses the OT technique for multiplication to increase the communication overhead. In this study, We use the new secret sharing technique to reduce the communication overhead and the online rounds.…”

Section: Related Workmentioning

confidence: 99%

PPCNN: An efficient privacy‐preserving CNN training and inference framework

Zhao

Wang

2022

Int J of Intelligent Sys

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Convolutional neural network (CNN) is one of the representative models of deep learning, commonly used to analyze visual images. CNN model is more accurate when trained on large amounts of data from multiple sources, and the huge training cost makes the model much more valuable. However, data from various sources is often privacy‐sensitive. Therefore, the privacy of these data should be protected during CNN model training and inference. In this paper, we propose an efficient and secure two‐party computation (2PC) framework PPCNN for privacy‐preserving CNN training and inference. Specifically, we use a new secret sharing technique introduced in ABY2.0 to securely compute various computational tasks involved in the CNN training and inference processes. This secret sharing technique can significantly reduce the communication overhead. Meanwhile, we assign these computationally intensive tasks to cloud servers to reduce the computational burden on local devices. We demonstrate the security of these protocols in the semihonest model. In addition, we use the MP‐SPDZ library to simulate our PPCNN framework, and the experiments prove its high efficiency and accuracy.

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“…Other PPML approaches are based on SMC [3,11,24,28,29], where users upload their encrypted data or secret shared data to one or more servers which then train ML models or use trusted execution environments [18,30]. These approaches are different from PROV-FL in which the dataset does not leave the user system.…”

Section: Privacy-preserving Federated Learningmentioning

confidence: 99%

“…In view of these attacks, PPML protocols have been developed. Existing techniques for designing PPML protocols can be broadly classified into four categories: 1) secure multi-party computation techniques, e.g., [3,11,13,28,29,33], 2) homomorphic encryption, e.g., [24,26,35], 3) differential privacy and homomorphic encryption or secure aggregation, e.g., [9,40,42], and 4) leveraging trusted execution environments (e.g., Intel-SGX), e.g., [18,30]. In the private training using secure multi-party computation, the training data is shared using a secret-sharing protocol among a small set of servers (e.g., 2, 3 or 4-server) (e.g., [11,28,29]), and then the training is conducted and the model is secret-shared among participating servers.…”

Section: Introductionmentioning

confidence: 99%