Privacy protection framework for open data: Constructing and assessing an effective approach

IF 2.4 3区 管理学 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE Library & Information Science Research Pub Date : 2024-07-01 DOI:10.1016/j.lisr.2024.101312
Yunjie Tang
{"title":"Privacy protection framework for open data: Constructing and assessing an effective approach","authors":"Yunjie Tang","doi":"10.1016/j.lisr.2024.101312","DOIUrl":null,"url":null,"abstract":"<div><p>Open data has revolutionized knowledge-sharing, providing economic and cultural benefits worldwide. However, releasing government, personal, or research data often raises concerns about data security and ethical implications, leading to infringements on privacy and related disputes. The Privacy Protection Framework for Open Data (PPFOD) is proposed to address these challenges. This framework aims to establish clear privacy protection measures and safeguard individuals' privacy rights. Existing privacy protection practices were examined using content analysis, and 36 indicators across five dimensions were developed and validated through an empirical study with 437 participants. The PPFOD offers comprehensive guidelines for data openness, empowering individuals to identify privacy risks, guiding businesses to ensure legal compliance and prevent data leaks, and assisting libraries and data institutions in implementing effective privacy education and training programs, fostering a more privacy-conscious and secure data era.</p></div>","PeriodicalId":47618,"journal":{"name":"Library & Information Science Research","volume":"46 3","pages":"Article 101312"},"PeriodicalIF":2.4000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Library & Information Science Research","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0740818824000331","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Open data has revolutionized knowledge-sharing, providing economic and cultural benefits worldwide. However, releasing government, personal, or research data often raises concerns about data security and ethical implications, leading to infringements on privacy and related disputes. The Privacy Protection Framework for Open Data (PPFOD) is proposed to address these challenges. This framework aims to establish clear privacy protection measures and safeguard individuals' privacy rights. Existing privacy protection practices were examined using content analysis, and 36 indicators across five dimensions were developed and validated through an empirical study with 437 participants. The PPFOD offers comprehensive guidelines for data openness, empowering individuals to identify privacy risks, guiding businesses to ensure legal compliance and prevent data leaks, and assisting libraries and data institutions in implementing effective privacy education and training programs, fostering a more privacy-conscious and secure data era.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
开放数据的隐私保护框架:构建和评估有效方法
开放数据彻底改变了知识共享,为全世界带来了经济和文化利益。然而,发布政府、个人或研究数据往往会引发对数据安全和道德影响的担忧,导致侵犯隐私和相关纠纷。为应对这些挑战,我们提出了开放数据隐私保护框架(PPFOD)。该框架旨在制定明确的隐私保护措施,保障个人的隐私权。我们利用内容分析法对现有的隐私保护实践进行了研究,制定了五个维度的 36 个指标,并通过对 437 名参与者的实证研究进行了验证。PPFOD 为数据开放提供了全面的指导方针,使个人有能力识别隐私风险,指导企业确保合法合规并防止数据泄露,同时协助图书馆和数据机构实施有效的隐私教育和培训计划,促进建立一个更具隐私意识和更安全的数据时代。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Library & Information Science Research
Library & Information Science Research INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
4.60
自引率
6.90%
发文量
51
期刊介绍: Library & Information Science Research, a cross-disciplinary and refereed journal, focuses on the research process in library and information science as well as research findings and, where applicable, their practical applications and significance. All papers are subject to a double-blind reviewing process.
期刊最新文献
Editorial Board Virtual reality training for crisis communication: Fostering empathy, confidence, and de-escalation skills in library and information science graduate students Beyond surface: Chinese youth's digital reading motivation explored via laddering and the interpretative structural modeling method (ISM) “O brave new world”1: A case study of a social worker in the public library Privacy protection framework for open data: Constructing and assessing an effective approach
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1