Loading…
Semantic Tree Based PPDP Technique for Multiple Sensitive Attributes in Inter Cloud
Digital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various a...
Saved in:
Published in: | SN computer science 2024-07, Vol.5 (6), p.722, Article 722 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | cdi_FETCH-LOGICAL-c1597-4bb36fcbf8da3d672ce218ddcefc3cb84c739aaf8f9ddc6d2bc358ca9e1b83d33 |
container_end_page | |
container_issue | 6 |
container_start_page | 722 |
container_title | SN computer science |
container_volume | 5 |
creator | Gadad, Veena Sowmyarani, C. N. Dayananda, P. |
description | Digital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various analyses, conduct research, and make critical decisions. Unauthorized users of the published data may violate privacy, notwithstanding the benefits. Data anonymization is one of the technique for achieving data privacy. Existing techniques consider single sensitive attribute and data is anonymized using generalization or suppression approaches. On observation, it is found that these techniques are less efficient since the collected data contains multiple sensitive attributes when anonymized using the same approaches leads to higher information loss and residue records. In this paper, multiple sensitive attributes are considered and the dataset is anonymized by constructing a semantic hierarchical tree it is further partitioned using the anatomy approach. Later, the partitions are stored in interclouds to achieve better privacy protection. Experiments are conducted to observe and analyze the computational performance, residue records and diversity percentage. The results obtained prove that the proposed technique is efficient when compared to the existing ones. |
doi_str_mv | 10.1007/s42979-024-03079-7 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3085041269</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3085041269</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1597-4bb36fcbf8da3d672ce218ddcefc3cb84c739aaf8f9ddc6d2bc358ca9e1b83d33</originalsourceid><addsrcrecordid>eNp9kF1LwzAUhoMoOOb-gFcBr6tp0jbJ5Zxfg4mDzeuQJiea0bUzaQX_vdEKeuXVeTm87_l4EDrPyWVOCL-KBZVcZoQWGWEkKX6EJrSq8kxIwo__6FM0i3FHCKElKYqqnKDNBva67b3B2wCAr3UEi9frmzXegnlt_dsA2HUBPw5N7w8N4A200ff-HfC874Ovhx4i9i1etj0EvGi6wZ6hE6ebCLOfOkXPd7fbxUO2erpfLuarzOSl5FlR16xypnbCamYrTg3QXFhrwBlmalEYzqTWTjiZmpWltWGlMFpCXgtmGZuii3HuIXTpztirXTeENq1UjIj0YU4rmVx0dJnQxRjAqUPwex0-VE7UFz818lOJn_rmp3gKsTEUk7l9gfA7-p_UJ-4Oc8s</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3085041269</pqid></control><display><type>article</type><title>Semantic Tree Based PPDP Technique for Multiple Sensitive Attributes in Inter Cloud</title><source>Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List</source><creator>Gadad, Veena ; Sowmyarani, C. N. ; Dayananda, P.</creator><creatorcontrib>Gadad, Veena ; Sowmyarani, C. N. ; Dayananda, P.</creatorcontrib><description>Digital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various analyses, conduct research, and make critical decisions. Unauthorized users of the published data may violate privacy, notwithstanding the benefits. Data anonymization is one of the technique for achieving data privacy. Existing techniques consider single sensitive attribute and data is anonymized using generalization or suppression approaches. On observation, it is found that these techniques are less efficient since the collected data contains multiple sensitive attributes when anonymized using the same approaches leads to higher information loss and residue records. In this paper, multiple sensitive attributes are considered and the dataset is anonymized by constructing a semantic hierarchical tree it is further partitioned using the anatomy approach. Later, the partitions are stored in interclouds to achieve better privacy protection. Experiments are conducted to observe and analyze the computational performance, residue records and diversity percentage. The results obtained prove that the proposed technique is efficient when compared to the existing ones.</description><identifier>ISSN: 2661-8907</identifier><identifier>ISSN: 2662-995X</identifier><identifier>EISSN: 2661-8907</identifier><identifier>DOI: 10.1007/s42979-024-03079-7</identifier><language>eng</language><publisher>Singapore: Springer Nature Singapore</publisher><subject>Advanced Computing and Data Sciences ; Algorithms ; Bronchitis ; Cardiovascular disease ; Computer Imaging ; Computer Science ; Computer Systems Organization and Communication Networks ; Data analysis ; Data collection ; Data Structures and Information Theory ; Datasets ; Gender ; Health care ; Heart ; Information systems ; Information Systems and Communication Service ; Original Research ; Pattern Recognition and Graphics ; Privacy ; Publishing ; Residues ; Semantics ; Software Engineering/Programming and Operating Systems ; Vision</subject><ispartof>SN computer science, 2024-07, Vol.5 (6), p.722, Article 722</ispartof><rights>The Author(s) 2024</rights><rights>The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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-c1597-4bb36fcbf8da3d672ce218ddcefc3cb84c739aaf8f9ddc6d2bc358ca9e1b83d33</cites><orcidid>0000-0001-8445-3469</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,786,790,27957,27958</link.rule.ids></links><search><creatorcontrib>Gadad, Veena</creatorcontrib><creatorcontrib>Sowmyarani, C. N.</creatorcontrib><creatorcontrib>Dayananda, P.</creatorcontrib><title>Semantic Tree Based PPDP Technique for Multiple Sensitive Attributes in Inter Cloud</title><title>SN computer science</title><addtitle>SN COMPUT. SCI</addtitle><description>Digital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various analyses, conduct research, and make critical decisions. Unauthorized users of the published data may violate privacy, notwithstanding the benefits. Data anonymization is one of the technique for achieving data privacy. Existing techniques consider single sensitive attribute and data is anonymized using generalization or suppression approaches. On observation, it is found that these techniques are less efficient since the collected data contains multiple sensitive attributes when anonymized using the same approaches leads to higher information loss and residue records. In this paper, multiple sensitive attributes are considered and the dataset is anonymized by constructing a semantic hierarchical tree it is further partitioned using the anatomy approach. Later, the partitions are stored in interclouds to achieve better privacy protection. Experiments are conducted to observe and analyze the computational performance, residue records and diversity percentage. The results obtained prove that the proposed technique is efficient when compared to the existing ones.</description><subject>Advanced Computing and Data Sciences</subject><subject>Algorithms</subject><subject>Bronchitis</subject><subject>Cardiovascular disease</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Computer Systems Organization and Communication Networks</subject><subject>Data analysis</subject><subject>Data collection</subject><subject>Data Structures and Information Theory</subject><subject>Datasets</subject><subject>Gender</subject><subject>Health care</subject><subject>Heart</subject><subject>Information systems</subject><subject>Information Systems and Communication Service</subject><subject>Original Research</subject><subject>Pattern Recognition and Graphics</subject><subject>Privacy</subject><subject>Publishing</subject><subject>Residues</subject><subject>Semantics</subject><subject>Software Engineering/Programming and Operating Systems</subject><subject>Vision</subject><issn>2661-8907</issn><issn>2662-995X</issn><issn>2661-8907</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kF1LwzAUhoMoOOb-gFcBr6tp0jbJ5Zxfg4mDzeuQJiea0bUzaQX_vdEKeuXVeTm87_l4EDrPyWVOCL-KBZVcZoQWGWEkKX6EJrSq8kxIwo__6FM0i3FHCKElKYqqnKDNBva67b3B2wCAr3UEi9frmzXegnlt_dsA2HUBPw5N7w8N4A200ff-HfC874Ovhx4i9i1etj0EvGi6wZ6hE6ebCLOfOkXPd7fbxUO2erpfLuarzOSl5FlR16xypnbCamYrTg3QXFhrwBlmalEYzqTWTjiZmpWltWGlMFpCXgtmGZuii3HuIXTpztirXTeENq1UjIj0YU4rmVx0dJnQxRjAqUPwex0-VE7UFz818lOJn_rmp3gKsTEUk7l9gfA7-p_UJ-4Oc8s</recordid><startdate>20240726</startdate><enddate>20240726</enddate><creator>Gadad, Veena</creator><creator>Sowmyarani, C. N.</creator><creator>Dayananda, P.</creator><general>Springer Nature Singapore</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><orcidid>https://orcid.org/0000-0001-8445-3469</orcidid></search><sort><creationdate>20240726</creationdate><title>Semantic Tree Based PPDP Technique for Multiple Sensitive Attributes in Inter Cloud</title><author>Gadad, Veena ; Sowmyarani, C. N. ; Dayananda, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1597-4bb36fcbf8da3d672ce218ddcefc3cb84c739aaf8f9ddc6d2bc358ca9e1b83d33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Advanced Computing and Data Sciences</topic><topic>Algorithms</topic><topic>Bronchitis</topic><topic>Cardiovascular disease</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Computer Systems Organization and Communication Networks</topic><topic>Data analysis</topic><topic>Data collection</topic><topic>Data Structures and Information Theory</topic><topic>Datasets</topic><topic>Gender</topic><topic>Health care</topic><topic>Heart</topic><topic>Information systems</topic><topic>Information Systems and Communication Service</topic><topic>Original Research</topic><topic>Pattern Recognition and Graphics</topic><topic>Privacy</topic><topic>Publishing</topic><topic>Residues</topic><topic>Semantics</topic><topic>Software Engineering/Programming and Operating Systems</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gadad, Veena</creatorcontrib><creatorcontrib>Sowmyarani, C. N.</creatorcontrib><creatorcontrib>Dayananda, P.</creatorcontrib><collection>Springer Nature OA/Free Journals</collection><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><jtitle>SN computer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gadad, Veena</au><au>Sowmyarani, C. N.</au><au>Dayananda, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Semantic Tree Based PPDP Technique for Multiple Sensitive Attributes in Inter Cloud</atitle><jtitle>SN computer science</jtitle><stitle>SN COMPUT. SCI</stitle><date>2024-07-26</date><risdate>2024</risdate><volume>5</volume><issue>6</issue><spage>722</spage><pages>722-</pages><artnum>722</artnum><issn>2661-8907</issn><issn>2662-995X</issn><eissn>2661-8907</eissn><abstract>Digital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various analyses, conduct research, and make critical decisions. Unauthorized users of the published data may violate privacy, notwithstanding the benefits. Data anonymization is one of the technique for achieving data privacy. Existing techniques consider single sensitive attribute and data is anonymized using generalization or suppression approaches. On observation, it is found that these techniques are less efficient since the collected data contains multiple sensitive attributes when anonymized using the same approaches leads to higher information loss and residue records. In this paper, multiple sensitive attributes are considered and the dataset is anonymized by constructing a semantic hierarchical tree it is further partitioned using the anatomy approach. Later, the partitions are stored in interclouds to achieve better privacy protection. Experiments are conducted to observe and analyze the computational performance, residue records and diversity percentage. The results obtained prove that the proposed technique is efficient when compared to the existing ones.</abstract><cop>Singapore</cop><pub>Springer Nature Singapore</pub><doi>10.1007/s42979-024-03079-7</doi><orcidid>https://orcid.org/0000-0001-8445-3469</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2661-8907 |
ispartof | SN computer science, 2024-07, Vol.5 (6), p.722, Article 722 |
issn | 2661-8907 2662-995X 2661-8907 |
language | eng |
recordid | cdi_proquest_journals_3085041269 |
source | Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List |
subjects | Advanced Computing and Data Sciences Algorithms Bronchitis Cardiovascular disease Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data analysis Data collection Data Structures and Information Theory Datasets Gender Health care Heart Information systems Information Systems and Communication Service Original Research Pattern Recognition and Graphics Privacy Publishing Residues Semantics Software Engineering/Programming and Operating Systems Vision |
title | Semantic Tree Based PPDP Technique for Multiple Sensitive Attributes in Inter Cloud |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-22T16%3A44%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Semantic%20Tree%20Based%20PPDP%20Technique%20for%20Multiple%20Sensitive%20Attributes%20in%20Inter%20Cloud&rft.jtitle=SN%20computer%20science&rft.au=Gadad,%20Veena&rft.date=2024-07-26&rft.volume=5&rft.issue=6&rft.spage=722&rft.pages=722-&rft.artnum=722&rft.issn=2661-8907&rft.eissn=2661-8907&rft_id=info:doi/10.1007/s42979-024-03079-7&rft_dat=%3Cproquest_cross%3E3085041269%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c1597-4bb36fcbf8da3d672ce218ddcefc3cb84c739aaf8f9ddc6d2bc358ca9e1b83d33%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3085041269&rft_id=info:pmid/&rfr_iscdi=true |