Abusing Social Networks for Automated User Profiling

Balduzzi, Marco; Platzer, Christian; Holz, Thorsten; Kirda, Engin; Balzarotti, Davide; Kruegel, Christopher

doi:10.1007/978-3-642-15512-3_22

Cited by 106 publications

(74 citation statements)

References 29 publications

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“…There are two major research directions on the privacy and security issues in OSNs: (1) to reveal the privacy threats in OSNs by conducting surveys [16,17] and proposing attack models [26], information inference algorithms [6,8,9,13,14,19,28], de-anonymization algorithms [4,21], and re-identification algorithms [27]; and (2) to reinforce users' privacy by redesigning the OSN system structure [5,10,20,23] and conducting anonymization [22,25]. This paper investigates the privacy setting breaches, which belongs to (1).…”

Section: Related Workmentioning

confidence: 99%

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Unveiling Privacy Setting Breaches in Online Social Networks

Ruan

Yue

Wang

2013

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Users of online social networks (OSNs) share personal information with their peers. To manage the access to one's personal information, each user is enabled to configure its privacy settings. However, even though users are able to customize the privacy of their homepages, their private information could still be compromised by an attacker by exploiting their own and their friends' public profiles. In this paper, we investigate the unintentional privacy disclosure of an OSN user even with the protection of privacy setting. We collect more than 300,000 Facebook users' public information and assess their measurable privacy settings. Given only a user's public information, we propose strategies to uncover the user's private basic profile or connection information, respectively, and then quantify the possible privacy leakage by applying the proposed schemes to the real user data. We observe that although the majority of users configure their basic profiles or friend lists as private, their basic profiles can be inferred with high accuracy, and a significant portion of their friends can also be uncovered via their public information.

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Section: Related Workmentioning

confidence: 99%

“…Regarding information inference, there are profile mining [6,8,19,29] and link mining [13-15, 24, 28] approaches, both of which this paper explores. Zheleva et al [29] presented several classification models using links and group memberships to infer the target users' profiles.…”

Section: Related Workmentioning

confidence: 99%

Unveiling Privacy Setting Breaches in Online Social Networks

Ruan

Yue

Wang

2013

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…These risks are illustrated by Balduzzi et al (2010) and Jagatic et al (2007). If the e-mail addresses of users were made public, then spammers could crawl the OSNs and collect e-mail addresses and who they belong to from user profiles.…”

Section: Privacy Attacks Associated With Personal Details Disclosurementioning

confidence: 99%

A vulnerability evaluation framework for online social network profiles: axioms and propositions

Alim

Neagu

Ridley

2012

IJITST

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Abstract:Online social network (OSN) usage has led to personal details being presented on online profiles readily. This can cause profile owners to be vulnerable to social engineering attacks. Our approach to quantifying vulnerability consists of a model with three components: individual, relative and absolute vulnerabilities.The individual vulnerability is calculated by allocating weights to profile attribute values disclosed which may contribute towards the personal vulnerability of the profile owner. The relative vulnerability is the collective vulnerability of the profiles' friends. The absolute vulnerability is the overall vulnerability for the profile which considers the individual and relative vulnerabilities.This paper extends research done on axioms based on the vulnerability model, by stating propositions to explore the effects of different operators on the profiles relative and absolute vulnerabilities. The case studies show that our approach offers a formal background for estimating how attributes and operator changes influence the individual, relative and absolute vulnerability of OSN profiles.Keywords: vulnerability; online social networks; OSN; axioms; propositions; information disclosure. Daniel Neagu is Professor of Computing at the University of Bradford. His research interests include knowledge discovery, information retrieval, data mining applications in multidisciplinary projects (with a focus in online social networks, healthcare and web profiling) by fusion of human experts knowledge and AI tools. He has published over 100 peer-reviewed papers and is the A vulnerability evaluation framework for online social network profiles 179 principal investigator in projects funded by EU FP7, EPSRC,

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“…2 It is necessary to highlight possible risks associated with such self-disclosure tools. 6 It is envisioned that increased privacy awareness may encourage users to secure their data. 6 We evaluated users' awareness of their privacy on Facebook.…”

Section: Introductionmentioning

confidence: 99%

“…6 It is envisioned that increased privacy awareness may encourage users to secure their data. 6 We evaluated users' awareness of their privacy on Facebook. Our aim was to highlight social media privacy risks by using Facebook as a case study.…”

Section: Introductionmentioning

confidence: 99%

Privacy and user awareness on Facebook

Nyoni¹,

Velempini²

2018

S. Afr. J. Sci.

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Users’ privacy on social media platforms continues to be important as users face numerous threats to their personal data. Social media sites such as Facebook store large amounts of users’ personal data which make such sites prime targets for hackers. Research has shown that users have been subjected to privacy attacks in which hacked personal data are sold to online marketers. These incidents have prompted the need to protect users’ privacy against data theft by third parties. We investigated the privacy risks that social media users on Facebook face when online. The privacy awareness of regular users of Facebook was evaluated through the observation of their online activities. Facebook was selected as a case study because it is the largest and most popular social media platform in South Africa. A sample group of Facebook users was selected for this study based on their activeness (or frequency of posting, uploaded or liking) on the site. Findings indicate that users’ personal data can be obtained as they are publicly available on Facebook. The implication of this finding is that users lack adequate awareness on protection tools designed to protect their personal data, and as a result, they risk losing their data and privacy. Significance: • This study serves as an assessment tool for the privacy and security features of the social media site Facebook. This assessment tool can help users of social media sites to evaluate their own behaviour and usage patterns on Facebook. It can also assist social media site designers in considering the effectiveness of current measures, which are designed to ensure that the privacy and safety of users are protected.

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Abusing Social Networks for Automated User Profiling

Cited by 106 publications

References 29 publications

Unveiling Privacy Setting Breaches in Online Social Networks

Unveiling Privacy Setting Breaches in Online Social Networks

A vulnerability evaluation framework for online social network profiles: axioms and propositions

Privacy and user awareness on Facebook

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