Semantic-driven watermarking of relational textual databases
•Mark embedding through synonyms substitutions avoids to compromise data quality.•Controlling the number of times each mark is embedded contributes to high resiliency.•Multi-word textual attributes in relational data helps to protect their ownership.•Majority voting compensates lack of precision in...
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Veröffentlicht in: | Expert systems with applications 2021-04, Vol.167, p.114013, Article 114013 |
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creator | Pérez Gort, Maikel Lázaro Olliaro, Martina Cortesi, Agostino Feregrino Uribe, Claudia |
description | •Mark embedding through synonyms substitutions avoids to compromise data quality.•Controlling the number of times each mark is embedded contributes to high resiliency.•Multi-word textual attributes in relational data helps to protect their ownership.•Majority voting compensates lack of precision in word sense disambiguation algorithms.
In relational database watermarking, the semantic consistency between the original database and the distorted one is a challenging issue which is disregarded by most watermarking proposals, due to the well-known assumption for which a small amount of errors in the watermarked database is tolerable. We propose a semantic-driven watermarking approach of relational textual databases, which marks multi-word textual attributes, exploiting the synonym substitution technique for text watermarking together with notions in semantic similarity analysis, and dealing with the semantic perturbations provoked by the watermark embedding. We show the effectiveness of our approach through an experimental evaluation, highlighting the resulting capacity, robustness and imperceptibility watermarking requirements. We also prove the resilience of our approach with respect to the random synonym substitution attack. |
doi_str_mv | 10.1016/j.eswa.2020.114013 |
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In relational database watermarking, the semantic consistency between the original database and the distorted one is a challenging issue which is disregarded by most watermarking proposals, due to the well-known assumption for which a small amount of errors in the watermarked database is tolerable. We propose a semantic-driven watermarking approach of relational textual databases, which marks multi-word textual attributes, exploiting the synonym substitution technique for text watermarking together with notions in semantic similarity analysis, and dealing with the semantic perturbations provoked by the watermark embedding. We show the effectiveness of our approach through an experimental evaluation, highlighting the resulting capacity, robustness and imperceptibility watermarking requirements. We also prove the resilience of our approach with respect to the random synonym substitution attack.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2020.114013</identifier><language>eng</language><publisher>New York: Elsevier Ltd</publisher><subject>Digital watermarking ; Perturbation ; Relational data bases ; Relational databases ; Semantic similarity analysis ; Semantics ; Substitutes ; Watermarking</subject><ispartof>Expert systems with applications, 2021-04, Vol.167, p.114013, Article 114013</ispartof><rights>2020</rights><rights>Copyright Elsevier BV Apr 1, 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-a712a4825c8ca2b8c7567eb066d36ebf15de83af9eb0ca6686182d9b05bb94223</citedby><cites>FETCH-LOGICAL-c372t-a712a4825c8ca2b8c7567eb066d36ebf15de83af9eb0ca6686182d9b05bb94223</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.eswa.2020.114013$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids></links><search><creatorcontrib>Pérez Gort, Maikel Lázaro</creatorcontrib><creatorcontrib>Olliaro, Martina</creatorcontrib><creatorcontrib>Cortesi, Agostino</creatorcontrib><creatorcontrib>Feregrino Uribe, Claudia</creatorcontrib><title>Semantic-driven watermarking of relational textual databases</title><title>Expert systems with applications</title><description>•Mark embedding through synonyms substitutions avoids to compromise data quality.•Controlling the number of times each mark is embedded contributes to high resiliency.•Multi-word textual attributes in relational data helps to protect their ownership.•Majority voting compensates lack of precision in word sense disambiguation algorithms.
In relational database watermarking, the semantic consistency between the original database and the distorted one is a challenging issue which is disregarded by most watermarking proposals, due to the well-known assumption for which a small amount of errors in the watermarked database is tolerable. We propose a semantic-driven watermarking approach of relational textual databases, which marks multi-word textual attributes, exploiting the synonym substitution technique for text watermarking together with notions in semantic similarity analysis, and dealing with the semantic perturbations provoked by the watermark embedding. We show the effectiveness of our approach through an experimental evaluation, highlighting the resulting capacity, robustness and imperceptibility watermarking requirements. We also prove the resilience of our approach with respect to the random synonym substitution attack.</description><subject>Digital watermarking</subject><subject>Perturbation</subject><subject>Relational data bases</subject><subject>Relational databases</subject><subject>Semantic similarity analysis</subject><subject>Semantics</subject><subject>Substitutes</subject><subject>Watermarking</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kEtPwzAQhC0EEqXwBzhF4pzgR2I7Ui-o4iVV4gCcrY2zQQ5tUmy3hX-Pq3DmNNJoZjX7EXLNaMEok7d9geEABac8GaykTJyQGdNK5FLV4pTMaF2pvGSqPCcXIfSUMkWpmpHFK25giM7mrXd7HLIDRPQb8J9u-MjGLvO4hujGAdZZxO-4S9pChAYChkty1sE64NWfzsn7w_3b8ilfvTw-L-9WuRWKxxwU41BqXlltgTfaqkoqbKiUrZDYdKxqUQvo6uRZkFJLpnlbN7RqmrrkXMzJzXR368evHYZo-nHn06RgeEUFU6zmKqX4lLJ-DMFjZ7bepU9-DKPmSMn05kjJHCmZiVIqLaYSpv17h94E63Cw2DqPNpp2dP_VfwEegXAz</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Pérez Gort, Maikel Lázaro</creator><creator>Olliaro, Martina</creator><creator>Cortesi, Agostino</creator><creator>Feregrino Uribe, Claudia</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20210401</creationdate><title>Semantic-driven watermarking of relational textual databases</title><author>Pérez Gort, Maikel Lázaro ; Olliaro, Martina ; Cortesi, Agostino ; Feregrino Uribe, Claudia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-a712a4825c8ca2b8c7567eb066d36ebf15de83af9eb0ca6686182d9b05bb94223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Digital watermarking</topic><topic>Perturbation</topic><topic>Relational data bases</topic><topic>Relational databases</topic><topic>Semantic similarity analysis</topic><topic>Semantics</topic><topic>Substitutes</topic><topic>Watermarking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pérez Gort, Maikel Lázaro</creatorcontrib><creatorcontrib>Olliaro, Martina</creatorcontrib><creatorcontrib>Cortesi, Agostino</creatorcontrib><creatorcontrib>Feregrino Uribe, Claudia</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pérez Gort, Maikel Lázaro</au><au>Olliaro, Martina</au><au>Cortesi, Agostino</au><au>Feregrino Uribe, Claudia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Semantic-driven watermarking of relational textual databases</atitle><jtitle>Expert systems with applications</jtitle><date>2021-04-01</date><risdate>2021</risdate><volume>167</volume><spage>114013</spage><pages>114013-</pages><artnum>114013</artnum><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>•Mark embedding through synonyms substitutions avoids to compromise data quality.•Controlling the number of times each mark is embedded contributes to high resiliency.•Multi-word textual attributes in relational data helps to protect their ownership.•Majority voting compensates lack of precision in word sense disambiguation algorithms.
In relational database watermarking, the semantic consistency between the original database and the distorted one is a challenging issue which is disregarded by most watermarking proposals, due to the well-known assumption for which a small amount of errors in the watermarked database is tolerable. We propose a semantic-driven watermarking approach of relational textual databases, which marks multi-word textual attributes, exploiting the synonym substitution technique for text watermarking together with notions in semantic similarity analysis, and dealing with the semantic perturbations provoked by the watermark embedding. We show the effectiveness of our approach through an experimental evaluation, highlighting the resulting capacity, robustness and imperceptibility watermarking requirements. We also prove the resilience of our approach with respect to the random synonym substitution attack.</abstract><cop>New York</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2020.114013</doi><oa>free_for_read</oa></addata></record> |
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subjects | Digital watermarking Perturbation Relational data bases Relational databases Semantic similarity analysis Semantics Substitutes Watermarking |
title | Semantic-driven watermarking of relational textual databases |
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