Are You Really Hidden? Predicting Current City from Profile and Social Relationship

TL;DR

This paper presents a novel method to predict users' current city in Facebook based on public information, assesses exposure risks, and offers tools for users to evaluate their privacy vulnerability.

Contribution

The study introduces a new prediction approach for hidden current city in Facebook and models exposure risk, aiding users in privacy protection.

Findings

The prediction method outperforms existing approaches in accuracy.

The exposure estimator effectively assesses individual privacy risks.

Case studies demonstrate practical privacy protection strategies.

Abstract

Privacy has become a major concern in Online Social Networks (OSNs) due to threats such as advertising spam, online stalking and identity theft. Although many users hide or do not fill out their private attributes in OSNs, prior studies point out that the hidden attributes may be inferred from some other public information. Thus, users' private information could still be at stake to be exposed. Hitherto, little work helps users to assess the exposure probability/risk that the hidden attributes can be correctly predicted, let alone provides them with pointed countermeasures. In this article, we focus our study on the exposure risk assessment by a particular privacy-sensitive attribute - current city - in Facebook. Specifically, we first design a novel current city prediction approach that discloses users' hidden `current city' from their self-exposed information. Based on 371,913 Facebook users' data, we verify that our proposed prediction approach can predict users' current city more accurately than state-of-the-art approaches. Furthermore, we inspect the prediction results and model the current city exposure probability via some measurable characteristics of the self-exposed information. Finally, we construct an exposure estimator to assess the current city exposure risk for individual users, given their self-exposed information. Several case studies are presented to illustrate how to use our proposed estimator for privacy protection.