Interaction and Visualization Design for User Privacy Interface on Online Social Networks

Abstract

The number of users participating in online social networks is increasing significantly recently. As a result, the amount of information created and shared by them is exploding. On the one hand, sharing information online helps people stay in touch with each other, although virtually. But on the other hand, sharing too much information may lead to sensitive personal data being leaked unexpectedly. To protect their users' private information, online social network providers often employ technical methods like access control, cryptography, privacy preserving data processing, among others. Although these approaches are good enough for their designated purposes, they provide little to no protection when are used wrongly. To reduce the number of mistakes users may make, online social network providers also offer them visual interfaces, instead of lengthy and boring texts, for privacy settings selection and configuration. Unfortunately, private information is still shared publicly, with or without its owners' awareness. In this paper, we attempt to mitigate the privacy leakage problem by proposing a novel visual model for measuring and representing users' privacy in online social network environment and associated privacy controller for protecting it. A concrete instance of the model has been designed and implemented. A demonstration of the model instance has been executed for one of the biggest social networks, Facebook. Initial results indicate the effectiveness of the proposed model and its concrete instance. However, a more important and difficult problem is whether online social network providers are willing to apply these results, which may affect sharing activities and go against their business objectives.

An effective and elastic blockchain-based provenance preserving solution for the open data

“…Some data is only allowed to be shared under specific circumstances and just for a related field or organization’s limited scope. These kinds of data need to be categorized, assessed and identified using purpose to protect data privacy (Pape et al , 2015; Vanezi et al , 2019; Dang et al , 2020). Understanding and managing these data will help us adjust system behavior appropriately for each kind of data.…”