{"title":"Privacy protection framework for open data: Constructing and assessing an effective approach","authors":"","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":null,"pages":null},"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.
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.