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 |
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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. 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.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics12163491</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>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</subject><ispartof>Electronics (Basel), 2023-08, Vol.12 (16), p.3491</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c311t-76214a6bb18d90dd884a30bbc298cdb1c0e55d03a58226c86aa308ce935c53043</cites><orcidid>0000-0002-3450-3808 ; 0000-0002-0394-4635</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Liu, Xin</creatorcontrib><creatorcontrib>Wang, Ruxue</creatorcontrib><creatorcontrib>Luo, Dan</creatorcontrib><creatorcontrib>Xu, Gang</creatorcontrib><creatorcontrib>Chen, Xiubo</creatorcontrib><creatorcontrib>Xiong, Neal</creatorcontrib><creatorcontrib>Liu, Xiaomeng</creatorcontrib><title>Secure Computation Protocol of Text Similarity against Malicious Attacks for Text Classification in Deep-Learning Technology</title><title>Electronics (Basel)</title><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.</description><subject>Algorithms</subject><subject>Classification</subject><subject>Computation</subject><subject>Computational linguistics</subject><subject>Computer network protocols</subject><subject>Cryptography</subject><subject>Data security</subject><subject>Deep learning</subject><subject>Design</subject><subject>Language processing</subject><subject>Machine learning</subject><subject>Matching</subject><subject>Matching theory</subject><subject>Methods</subject><subject>Natural language interfaces</subject><subject>Natural language processing</subject><subject>Neural networks</subject><subject>Protocol</subject><subject>Similarity</subject><subject>Text categorization</subject><subject>Texts</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptUU1LxDAQLaLgsu4v8BLw3DUf_UiOy_oJKwq7nks6TWvWblKTFFzwxxupBw_OHGaYee_NwEuSS4KXjAl8rXoFwVmjwRNKCpYJcpLMKC5FKqigp3_682Th_R7HEIRxhmfJ11bB6BRa28MwBhm0NejF2WDB9si2aKc-A9rqg-6l0-GIZCe18QE9yV6DtqNHqxAkvHvUWjeh1730XrcaJjVt0I1SQ7pR0hltugiCN2N72x0vkrNW9l4tfus8eb273a0f0s3z_eN6tUmBERLSsqAkk0VdE94I3DScZ5LhugYqODQ1AazyvMFM5pzSAngh45qDEiyHnOGMzZOrSXdw9mNUPlR7OzoTT1aU5yUmQogyopYTqpO9qrRpbXASYjbqoMEa1eo4X8VvMo55TiOBTQRw1nun2mpw-iDdsSK4-rGm-sca9g2MMYY7</recordid><startdate>20230801</startdate><enddate>20230801</enddate><creator>Liu, Xin</creator><creator>Wang, Ruxue</creator><creator>Luo, Dan</creator><creator>Xu, Gang</creator><creator>Chen, Xiubo</creator><creator>Xiong, Neal</creator><creator>Liu, Xiaomeng</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0002-3450-3808</orcidid><orcidid>https://orcid.org/0000-0002-0394-4635</orcidid></search><sort><creationdate>20230801</creationdate><title>Secure Computation Protocol of Text Similarity against Malicious Attacks for Text Classification in Deep-Learning Technology</title><author>Liu, Xin ; 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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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