Fake identities in social media: A case study on the sustainability of the Facebook business model

  title={Fake identities in social media: A case study on the sustainability of the Facebook business model},
  author={Katharina Krombholz and Dieter Merkl and Edgar R. Weippl},
  journal={Journal of Service Science Research},
Social networks such as Facebook, Twitter and Google+ have attracted millions of users in the last years. One of the most widely used social networks, Facebook, recently had an initial public offering (IPO) in May 2012, which was among the biggest in Internet technology. Forprofit and nonprofit organizations primarily use such platforms for target-oriented advertising and large-scale marketing campaigns. Social networks have attracted worldwide attention because of their potential to address… 

Social Engineering Attacks on Facebook – A Case Study

An overview of Social Engineering Attacks on Facebook based on a variety of data collected is given to analyse how Facebook is misused and turned into an attack platform in order to get sensitive information that can be used to create an attack profile against an individual.


  • Computer Science
  • 2017
The minimal set of profile data that are necessary for identifying Fake profiles in Facebook, Twitter and Sina weibo are identified and it is demonstrated that with limited profile data the approach can identify the fake profile with 84 % accuracy and 2.44 % false negative, comparable to the results obtained by other existing approaches based on the larger data set and more profile information.

Sybil Defense Techniques in Online Social Networks: A Survey

A comprehensive survey of literature from 2006 to 2016 on Sybil attacks in online social networks and use of social networks as a tool to analyze and prevent these attack types is provided.

A sneak into the Devil's Colony - Fake Profiles in Online Social Networks

Various types of OSN threat generators like compromised profiles, cloned profiles and online bots (spam bots, social bots, like bots and influential bots) have been classified and an attempt is made to present several categories of features that have been used to train classifiers in order to identify a fake profile.

Today's social network sites: An analysis of emerging security risks and their counter measures

A taxonomy of social websites attacks is introduced and literature survey results that help to categorize possible attacks and preferred counteractions to defense against these attacks are provided.

Social Network Analysis & Information Disclosure: A Case Study

The analysis of the results seem to suggest that the majority of users were mainly using Facebook, despite of concerns raised about the disclosure of personal information on social network sites, users continue to disclose huge quantity ofpersonal information, they find that reading privacy policy is time consuming and changes made can result into improper settings.

Utilizing Social Media in Modern Business

Social media has become one of the most popular applications over the Internet (Zhang, Lin, & Wang, 2013). Social networking sites (SNSs), such as Facebook and Twitter, gain the increasing popularity

Cyber Security Challenges in Social Media

This research has studied the ways in which social media platforms are inherently putting users in the way of security and privacy threats and formulated some recommendations that it hopes will reduce theSecurity and privacy issues that users are facing on social media.

Identifying Fake Profiles in LinkedIn

This research identifies the minimal set of profile data that are necessary for identifying fake profiles in LinkedIn and identifies the appropriate data mining approach for such task and demonstrates that with limited profile data this approach can identify the fake profile with 84% accuracy and only 2.44% false negative.

Developing a Regulatory and Control Frameworks for Enhancing the Security of Facebook Users

The developed framework aims to restrict the scammers from creating fake profiles and to make the user verified and authenticated on the Facebook by using the unique attribute of the user, and enforces the users to use only real and verifiable information.



Privacy concerns and identity in online social networks

It is found that users tend to reduce the Amount of information disclosed as a response to their concerns regarding Organizational Threats, and become more conscious about the information they reveal as a result of Social Threats.

The socialbot network: when bots socialize for fame and money

This paper adopts a traditional web-based botnet design and built a Socialbot Network (SbN): a group of adaptive socialbots that are orchestrated in a command-and-control fashion that is evaluated how vulnerable OSNs are to a large-scale infiltration by socialbots.

Analyzing facebook privacy settings: user expectations vs. reality

A survey is deployed to 200 Facebook users recruited via Amazon Mechanical Turk, finding that 36% of content remains shared with the default privacy settings, and overall, privacy settings match users' expectations only 37% of the time, and when incorrect, almost always expose content to more users than expected.

All your contacts are belong to us: automated identity theft attacks on social networks

This paper investigates how easy it would be for a potential attacker to launch automated crawling and identity theft attacks against a number of popular social networking sites in order to gain access to a large volume of personal user information.

Information revelation and privacy in online social networks

This paper analyzes the online behavior of more than 4,000 Carnegie Mellon University students who have joined a popular social networking site catered to colleges and evaluates the amount of information they disclose and study their usage of the site's privacy settings.

De-anonymizing Social Networks

A framework for analyzing privacy and anonymity in social networks is presented and a new re-identification algorithm targeting anonymized social-network graphs is developed, showing that a third of the users who can be verified to have accounts on both Twitter and Flickr can be re-identified in the anonymous Twitter graph.

To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles

This work shows how an adversary can exploit an online social network with a mixture of public and private user profiles to predict the private attributes of users, and proposes practical models that use friendship and group membership information to infer sensitive attributes.

Loose tweets: an analysis of privacy leaks on twitter

The nature of privacy leaks on Twitter is characterized to gain an understanding of what types of private information people are revealing on it and automatic classifiers are built to detect incriminating tweets for these three topics in real time in order to demonstrate the real threat posed to users by, e.g., burglars and law enforcement.

StarClique: guaranteeing user privacy in social networks against intersection attacks

This paper identifies StarClique, a locally minimal graph structure required for users to attain k-anonymity, where at worst, a user is identified as one of k possible contributors of a data object, and proposes anonymization techniques to protect users.

Turning privacy leaks into floods: surreptitious discovery of social network friendships and other sensitive binary attribute vectors

This work studies methods for attacking the privacy of social networking sites, collaborative filtering sites, databases of genetic signatures, and other data sets that can be represented as vectors of binary relationships using theoretical characterizations as well as experimental tests.