Shared data granularity: A latent dimension of privacy scoring over online social networks

TL;DR

This paper introduces a new privacy scoring method for online social networks, based on real-world LinkedIn data, considering data granularity, and demonstrates its effectiveness through extensive experiments.

Contribution

It presents a novel privacy scoring approach that accounts for data granularity, using real-world LinkedIn data, and provides a comprehensive evaluation of existing and new methods.

Findings

Proposed a new privacy scoring method considering data granularity.

Validated the effectiveness of the new method through extensive experiments.

Analyzed privacy risks using real-world LinkedIn data.

Abstract

Privacy scoring aims at measuring the privacy violation risk of a user over an online social network (OSN). Existing work in the field rely on possibly biased or emotional survey data and focus only on personel purpose OSNs like Facebook. In contrast to existing work, in this thesis, we work with real-world OSN data collected from LinkedIn, the most popular professional-purpose OSN (ProOSN). Towards this end, we developed an extensive crawler to collect all relevant profile data of 5,389 LinkedIn users, modelled these data using both relational and graph databases and quantitatively analyzed all privacy risk scoring methods in the literature. Additionally, we propose a novel scoring method that consider the granularity of data an OSN user shares on her profile page. Extensive experimental evaluation of existing and proposed scoring methods indicates the effectiveness of the proposed solution.