Metrics for Privacy Assessment When Sharing Information in Online Social Networks

Abstract

Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts' scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures.

Towards Content Sensitivity Analysis

“…With the success of online social networks and content sharing platforms, understanding and measuring the exposure of user privacy in the Web has become crucial [11,12]. Thus, many different metrics and methods have been proposed with the goal of assessing the risk of privacy leakage in posting activities [1,23]. Most research efforts, however, focus on measuring the overall exposure of users according to their privacy settings [8,19] or position within the network [18].…”

Measures of Privacy Protection on Social Environments

“…Finally, the participants considered for the experiments have a certain age distribution (approx. [12][13][14]. Therefore, these results cannot be extrapolated to users that are in other age range.…”

“…Chapter 4 (previously published in [13]) proposes an extension of the privacy metric with new metrics that estimate the depth and width of users' dissemina-8 CHAPTER 1. INTRODUCTION tion power.…”

“…All of these works converge to a clear premise: not all of the personal information has the same value. Legislation such as the GDPR 11 , NIST 12 , or UKAN 13 (which emerged from the need to protect users' data) distinguishes different levels of data sensitivity. Companies that buy, sell, and exchange users' data as an economic resource also consider different values for data based on the kind of information provided and whether they can link it to other data [172].…”

“…PESEDIA was designed as a tool for the teaching/learning of OSN features and to increase concern, awareness, and seeking behavior on privacy, especially in the case of children and teenagers who are just beginning to use OSNs. The main goals of PESEDIA include: (i) the design and development of new metrics to analyze and quantify privacy risks [11,13]; (ii) the application of methods to influence users' behavior towards safer actions regarding their privacy [12]; and (iii) the evaluation and testing of new proposals with real users [14]. The underlying implementation of PESEDIA uses Elgg [64], which is an open-source engine that is used to build social environments.…”