Community Detection for Access-Control Decisions: Analysing the Role of Homophily and Information Diffusion in Online Social Networks

TL;DR

This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs and analyses the performance of the aforementioned approach under different homophily conditions through a simulation model.

Abstract

Access-Control Lists (ACLs) (a.k.a. friend lists) are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as untrusted) to the emerging communities inside the user's ego-network. However, unlike users' access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination.

Figures (7)

• Figure 1: Alice's ego-network. Untrusted friends John, Bill, and Bob are grouped under cluster $C_1$. An ACL containing all the nodes in $C_1$ is generated and recommended to Alice diaz2018access.
• Figure 2: Simulation output for homophily conditions $H_1$, $H_2$, and $H_3$.
• Figure 3: Simulation approach for the generation of ACLs diaz2018access.
• Figure 4: Attributes probability distribution diaz2018access.
• Figure 5: Simulation approach for information diffusion.