The PViz comprehension tool for social network privacy settings

Abstract

Users' mental models of privacy and visibility in social networks often involve subgroups within their local networks of friends. Many social networking sites have begun building interfaces to support grouping, like Facebook's lists and "Smart Lists," and Google+'s "Circles." However, existing policy comprehension tools, such as Facebook's Audience View, are not aligned with this mental model. In this paper, we introduce PViz, an interface and system that corresponds more directly with how users model groups and privacy policies applied to their networks. PViz allows the user to understand the visibility of her profile according to automaticallyconstructed, natural sub-groupings of friends, and at different levels of granularity. Because the user must be able to identify and distinguish automatically-constructed groups, we also address the important sub-problem of producing effective group labels. We conducted an extensive user study comparing PViz to current policy comprehension tools (Facebook's Audience View and Custom Settings page). Our study revealed that PViz was comparable to Audience View for simple tasks, and provided a significant improvement for complex, group-based tasks, despite requiring users to adapt to a new tool. Utilizing feedback from the user study, we further iterated on our design, constructing PViz 2.0, and conducted a follow-up study to evaluate our refinements.

A Survey on A3P System for Image Uploading

“…Fang et al [5] proposed a privacy wizard to help users grant privileges to their friends. The wizard asks users to first assign privacy labels to selected friends, and then uses this as input to construct a classifier which classifies friends based on their profiles and automatically assign privacy labels to the unlabeled friends.…”

Survey on Access Control for Community-Centered Collaborative Systems

“…For example, Facebook provides the "View As" tool that allows a user to see how its profile appears to the public, friends or a specific social network user. 3 Other tools have been proposed to support users in configuring their social network privacy settings like PViz [Mazzia et al 2012] and VeilMe [Wang et al 2015]. PViz provides a graphical interface based on a bubble chart to visualize sharing settings in Facebook and provides support to generate social groups from public profile information automatically in order to reduce configuration efforts (Figure 2a).…”

“…MController [Hu et al 2013] is a voting-based tool for the collaborative management of shared resources (Figure 3a). MController allows the owner of a shared photo to select and configure the conflict resolution mechanism, possibly accounting for the sharing preferences of the other con-(a) PViz [Mazzia et al 2012] (b) VeilMe [Wang et al 2015] trollers of the photo. Retinue [Hu et al 2011] is a risk-based collaborative data sharing mechanism that allows a systematic detection and resolution of multi-party conflicts in online social networks (Figure 3b).…”

Survey on Privacy Policy Specification System for User Uploaded Images Over Popular Content Sharing Sites

“…One of the main reasons provided is that given the amount of shared information this process can be tedious and error-prone. Therefore, many have acknowledged the need of policy recommendation systems which can help users to easily and properly configure privacy settings [4], [3], [5], [6]. However, existing proposals for automating privacy settings appear to be insufficient to address the unique privacy needs of images, due to the amount of information implicitly carried within images, and their relationship with the online environment wherein they are exposed.…”

“…Ravichandran et al [6] studied how to predict a user's privacy preferences or location-based data (i.e., share her location or not) based on location and time of day. Fang et al [5] proposed a privacy wizard to help users grant privileges to their friends. The wizard asks users to first assign privacy labels to selected friends, and then uses this as input to construct a classifier which classifies friends based on their profiles and automatically assign privacy labels to the unlabeled friends.…”