User identities across social networks: quantifying linkability and nudging users to control linkability

Abstract

The Online Social Network (OSN) landscape has transformed significantly over the past few years with the emergence of networks. The primary capabilities of these online networks differ. Few of the major leading ones are: Relationship networks (Facebook), Media sharing networks (Instagram), Online reviews (Zomato), Discussion forums (Quora), Social publishing platforms (Twitter), etc. In order to avail these services, users end up creating multiple identities across these platforms. For each OSN, a user defines his identity with a different set of attributes, genre of content and friends to suit the purpose of using that OSN. Researchers have proposed numerous techniques to resolve multiple such identities of a user across different platforms. However, the ability to link different identities poses a threat to the users privacy; users may or may not want their identities to be linkable across networks. In this study, we model the notion of linkability as the probability of an adversary (who is part of the users network) being able to link two profiles across different platforms, to the same real user. The major factors that lead to increased linkability across social networks are similar profile attributes and cross posting across the social networks. To make users aware of the linkability across multiple social networks, as part of the thesis, we develop a framework, which assists the users to control their linkability. It has two components; a linkability calculator that uses three state-of-the-art identity resolution techniques to compute a normalized linkability measure for each pair of social network platforms used by a user, and a soft paternalistic nudge. The user configures the desired linkability score range for each pair of networks. There are two types of nudge: Attribute-driven Notification Nudge, which alerts the user through a pop-up notification if any of their activity violates their preferred linkability score range and Content-driven Color Nudge, which notifies the user by changing the color of the box bounding the post update from black to red if the content being posted by them is found to be similar to the content already posted by them on a different social network. We evaluate the effectiveness of the nudge by conducting a controlled user study on privacy conscious users who maintain their accounts on Facebook, Twitter, and Instagram. Outcomes of user study confirmed that the proposed framework of providing nudge helped 75% of participants to take informed decisions, thereby preventing inadvertent exposure of their personal information across social network services. Also, the content driven color nudge refrained few participants from making post updates.