Topic
Identity theft
About: Identity theft is a research topic. Over the lifetime, 2284 publications have been published within this topic receiving 31700 citations.
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TL;DR: A novel current city prediction approach is designed that discloses users' hidden `current city' from their self-exposed information and can predict users' current city more accurately than state-of-the-art approaches.
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.
2 citations
Journal Article•
TL;DR: As identity theft and other internet crimes become rampant, it is very important that libraries build a high level of trust with their users.
Abstract: As identity theft and other internet crimes become rampant, it's very important that libraries build a high level of trust with theirusers.
2 citations
11 Sep 2017
TL;DR: Considering deleted posts as an explicit manifestation of users’ regrets, an Inductive Logic Programming (ILP) approach for learning privacy heuristics is proposed.
Abstract: Disclosing private information in Social Network Sites (SNSs) often results in unwanted incidents for the users (such as bad image, identity theft, or unjustified discrimination), along with a feeling of regret and repentance. Regrettable online self-disclosure experiences can be seen as sources of privacy heuristics (best practices) that can help shaping better privacy awareness mechanisms. Considering deleted posts as an explicit manifestation of users’ regrets, we propose an Inductive Logic Programming (ILP) approach for learning privacy heuristics. In this paper we introduce the motivating scenario and the theoretical foundations of this approach, and we provide an initial assessment towards its implementation.
2 citations
22 Jun 2013
TL;DR: In this paper, the authors present possible solutions and recommendations to be aware and avoid this kind of attacks that are a high risk for the security of students and teachers in the university's WLAN.
Abstract: This paper is about the insecurity of the Wireless LAN of a university that is supposed to be available only for students and teachers through a username and a password. This attack shows how to deceive a user making him think he is connecting to a real access point and entering his information in the real web interface that the university provides for user authentication. We create a fake access point using the same name of the university's WLAN to capture login credentials using a fake authentication web interface and then use this information for identity theft. After the demonstration we present possible solutions and recommendations to be aware and avoid this kind of attacks that are a high risk for the security of students and teachers.
2 citations
2 citations