Secure Computation Protocol of Text Similarity against Malicious Attacks for Text Classification in Deep-Learning Technology

With the development of deep learning, the demand for similarity matching between texts in text classification is becoming increasingly high. How to match texts quickly under the premise of keeping private information secure has become a research hotspot. However, most existing protocols currently h...

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Veröffentlicht in:Electronics (Basel) 2023-08, Vol.12 (16), p.3491
Hauptverfasser: Liu, Xin, Wang, Ruxue, Luo, Dan, Xu, Gang, Chen, Xiubo, Xiong, Neal, Liu, Xiaomeng
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container_issue 16
container_start_page 3491
container_title Electronics (Basel)
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creator Liu, Xin
Wang, Ruxue
Luo, Dan
Xu, Gang
Chen, Xiubo
Xiong, Neal
Liu, Xiaomeng
description With the development of deep learning, the demand for similarity matching between texts in text classification is becoming increasingly high. How to match texts quickly under the premise of keeping private information secure has become a research hotspot. However, most existing protocols currently have full set limitations, and the applicability of these methods is limited when the data size is large and scattered. Therefore, this paper applies the secure vector calculation method for text similarity matching in the case of data without any complete set constraints, and it designs a secure computation protocol of text similarity (SCTS) based on the semi-honest model. At the same time, elliptic-curve cryptography technology is used to greatly improve the execution efficiency of the protocol. In addition, we also analyzed the possibility of the malicious behavior of participants in the semi-honest-model protocol, and further designed an SCTS protocol suitable for the malicious model using the cut-and-choose and zero-knowledge-proof methods. By proposing a security mechanism, this protocol aims to provide a reliable and secure computing solution that can effectively prevent malicious attacks and interference. Finally, through the analysis of the efficiencies of the existing protocols, the efficiencies of the protocols under the malicious model are further verified, and the practical value for text classification in deep learning is demonstrated.
doi_str_mv 10.3390/electronics12163491
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How to match texts quickly under the premise of keeping private information secure has become a research hotspot. However, most existing protocols currently have full set limitations, and the applicability of these methods is limited when the data size is large and scattered. Therefore, this paper applies the secure vector calculation method for text similarity matching in the case of data without any complete set constraints, and it designs a secure computation protocol of text similarity (SCTS) based on the semi-honest model. At the same time, elliptic-curve cryptography technology is used to greatly improve the execution efficiency of the protocol. In addition, we also analyzed the possibility of the malicious behavior of participants in the semi-honest-model protocol, and further designed an SCTS protocol suitable for the malicious model using the cut-and-choose and zero-knowledge-proof methods. 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source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; MDPI - Multidisciplinary Digital Publishing Institute
subjects Algorithms
Classification
Computation
Computational linguistics
Computer network protocols
Cryptography
Data security
Deep learning
Design
Language processing
Machine learning
Matching
Matching theory
Methods
Natural language interfaces
Natural language processing
Neural networks
Protocol
Similarity
Text categorization
Texts
title Secure Computation Protocol of Text Similarity against Malicious Attacks for Text Classification in Deep-Learning Technology
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