Privacy Scoring over OSNs: Shared Data Granularity as a Latent Dimension
Authors: 
Yasir Kilic, Ali InanAuthors Info & Claims
Yasir Kilic, Ali InanAuthors Info & ClaimsArticle No.: 30, Pages 1 - 28
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 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.
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Publication History
Published: 10 October 2023
Online AM: 17 June 2023
Accepted: 26 May 2023
Revised: 29 March 2023
Received: 07 April 2022
Published in TWEB Volume 17, Issue 4
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