2021/05/01 by Yasir Kilic, Kilic, Yasir
Computer Science · Social Sciences · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2105.07845
openalex publication_date 2021/05/01 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Privacy scoring aims at measuring the privacy violation risk of a user over\nan online social network (OSN). Existing work in the field rely on possibly\nbiased or emotional survey data and focus only on personel purpose OSNs like\nFacebook. In contrast to existing work, in this thesis, we work with real-world\nOSN data collected from LinkedIn, the most popular professional-purpose OSN\n(ProOSN). Towards this end, we developed an extensive crawler to collect all\nrelevant profile data of 5,389 LinkedIn users, modelled these data using both\nrelational and graph databases and quantitatively analyzed all privacy risk\nscoring methods in the literature. Additionally, we propose a novel scoring\nmethod that consider the granularity of data an OSN user shares on her profile\npage. Extensive experimental evaluation of existing and proposed scoring\nmethods indicates the effectiveness of the proposed solution.\n