{"title":"Privacy Scoring Over OSNs: Shared Data Granularity as a Latent Dimension","authors":"Yasir Kilic, Ali Inan","doi":"https://dl.acm.org/doi/10.1145/3604909","DOIUrl":null,"url":null,"abstract":"<p>Privacy scoring aims at measuring the privacy violation risk of a user over an online social network (OSN) based on attribute values shared in the user’s OSN profile page and the user’s position in the network. Existing studies on privacy scoring rely on possibly biased or emotional survey data. In this study, we work with real-world data collected from the professional LinkedIn OSN and show that probabilistic scoring models derived from the item response theory (IRT) fit real-world data better than naive approaches. We also introduce the granularity of the data an OSN user shares on her profile as a latent dimension of the OSN privacy scoring problem. Incorporating data granularity into our model, we build the most comprehensive solution to the OSN privacy scoring problem. Extensive experimental evaluation of various scoring models indicate the effectiveness of the proposed solution.</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":"43 11","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on the Web","FirstCategoryId":"94","ListUrlMain":"https://doi.org/https://dl.acm.org/doi/10.1145/3604909","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Privacy scoring aims at measuring the privacy violation risk of a user over an online social network (OSN) based on attribute values shared in the user’s OSN profile page and the user’s position in the network. Existing studies on privacy scoring rely on possibly biased or emotional survey data. In this study, we work with real-world data collected from the professional LinkedIn OSN and show that probabilistic scoring models derived from the item response theory (IRT) fit real-world data better than naive approaches. We also introduce the granularity of the data an OSN user shares on her profile as a latent dimension of the OSN privacy scoring problem. Incorporating data granularity into our model, we build the most comprehensive solution to the OSN privacy scoring problem. Extensive experimental evaluation of various scoring models indicate the effectiveness of the proposed solution.
隐私评分是根据用户的OSN配置页面中共享的属性值和用户在网络中的位置，衡量用户在某个OSN (online social network)上隐私被侵犯的风险。现有的隐私评分研究依赖于可能有偏见或情绪化的调查数据。在本研究中，我们使用从专业LinkedIn OSN收集的真实世界数据，并表明从项目反应理论(IRT)衍生的概率评分模型比朴素方法更适合真实世界数据。我们还引入了OSN用户在其个人资料上共享的数据粒度，作为OSN隐私评分问题的潜在维度。将数据粒度整合到我们的模型中，我们构建了最全面的OSN隐私评分问题解决方案。各种评分模型的广泛实验评估表明了所提出的解决方案的有效性。
期刊介绍：
Transactions on the Web (TWEB) is a journal publishing refereed articles reporting the results of research on Web content, applications, use, and related enabling technologies. Topics in the scope of TWEB include but are not limited to the following: Browsers and Web Interfaces; Electronic Commerce; Electronic Publishing; Hypertext and Hypermedia; Semantic Web; Web Engineering; Web Services; and Service-Oriented Computing XML.
In addition, papers addressing the intersection of the following broader technologies with the Web are also in scope: Accessibility; Business Services Education; Knowledge Management and Representation; Mobility and pervasive computing; Performance and scalability; Recommender systems; Searching, Indexing, Classification, Retrieval and Querying, Data Mining and Analysis; Security and Privacy; and User Interfaces.
Papers discussing specific Web technologies, applications, content generation and management and use are within scope. Also, papers describing novel applications of the web as well as papers on the underlying technologies are welcome.
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