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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 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 indicates the effectiveness of the proposed solution.<\/jats:p>","DOI":"10.1145\/3604909","type":"journal-article","created":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T08:41:58Z","timestamp":1686991318000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Privacy Scoring over OSNs: Shared Data Granularity as a Latent Dimension"],"prefix":"10.1145","volume":"17","author":[{"given":"Yasir","family":"Kilic","sequence":"first","affiliation":[{"name":"Adana Alparslan Turkes Science and Technology University, TR"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Inan","sequence":"additional","affiliation":[{"name":"Adana Alparslan Turkes Science and Technology University, TR"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,10]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.12.030"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781482276725"},{"issue":"9","key":"e_1_3_1_4_2","first-page":"1","article-title":"A privacy paradox: Social networking in the United States","volume":"11","author":"Barnes Susan B.","year":"2006","unstructured":"Susan B. 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