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Social Network Analysis and Mining
Erschienen in: Social Network Analysis and Mining 1/2014

01.12.2014 | Original Article

Friend or foe? Fake profile identification in online social networks

verfasst von: Michael Fire, Dima Kagan, Aviad Elyashar, Yuval Elovici

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2014

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Abstract

The amount of personal information involuntarily exposed by users on online social networks is staggering, as shown in recent research. Moreover, recent reports indicate that these networks are inundated with tens of millions of fake user profiles, which may jeopardize the user’s security and privacy. To identify fake users in such networks and to improve users’ security and privacy, we developed the Social Privacy Protector (SPP) software for Facebook. This software contains three protection layers that improve user privacy by implementing different methods to identify fake profiles. The first layer identifies a user’s friends who might pose a threat and then restricts the access these “friends” have to the user’s personal information. The second layer is an expansion of Facebook’s basic privacy settings based on different types of social network usage profiles. The third layer alerts users about the number of installed applications on their Facebook profile that has access to their private information. An initial version of the SPP software received positive media coverage, and more than 3,000 users from more than 20 countries have installed the software, out of which 527 have used the software to restrict more than 9,000 friends. In addition, we estimate that more than 100 users have accepted the software’s recommendations and removed nearly 1,800 Facebook applications from their profiles. By analyzing the unique dataset obtained by the software in combination with machine learning techniques, we developed classifiers that are able to predict Facebook profiles with a high probability of being fake and consequently threaten the user’s security and privacy. Moreover, in this study, we present statistics generated by the SPP software on both user privacy settings and the number of applications installed on Facebook profiles. These statistics alarmingly demonstrate how vulnerable Facebook users’ information is to both fake profile attacks and third-party Facebook applications.
Vorheriger Artikel Predicting political preference of Twitter users
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Fußnoten
6
Due to the unexpected number of downloads and high usage of the application, our servers did not succeed in supporting the massive number of users all at once. Moreover, in our initial version, the SPP Facebook application did not support all the existing web browsers. Therefore, many users who installed the SPP software were not able to use it on demand.
 
12
An initial version of the SPP software was described, as a work in progress, in our previous paper (Fire et al. 2012b).
 
17
In case more than \(k\) friends received the lowest Connection-Strength values, we randomly removed friends with the highest Connection-Strength values, until we were left with exactly \(k\) friends.
 
18
If a restricted user’s friend was presented in the recommendation interface and was restricted using the alphabetical interface, the link between the user and the restricted friend was assigned to the recommended restricted links set.
 
19
There can be cases in which SPP users choose to restrict Facebook friends who are legitimate Facebook users but received low Connection-Strength scores. Nevertheless, according to the Fake profiles dataset definition, and due to the SPP software’s original purpose, we assume that in most cases, the SPP users indeed chose to restrict fake profiles.
 
20
In some cases, we were not able to extract the user’s (\(v\)) friends number probably due to the \(v\)’s privacy settings.
 
21
In order to measure the classifiers’ training time, we used WEKA’s UserCPU_Time_training measure.
 
22
In case of the Friends-restriction datasets, we calculated the average users precision for 355 SPP application users only, ensuring they were familiar with the alphabetical interface and had used it to restrict their friends.
 
23
The SPP Add-on was available for download from several locations, such as the Firefox Add-ons website and the PrivacyProtector.net website. Due to the fact that not all locations store the number of downloads, we can only estimate the number of downloads according to our HTTP server logs.
 
24
Due to the fact that not all SPP users opened their Facebook privacy settings during this time period, and probably due to problems in parsing the different Facebook privacy settings page layouts, we succeeded in collecting the SPP users’ privacy settings for only a limited number of users.
 
25
The classifier’s true-positive rate is the proportion of links that were classified as restricted to all links which were actually were restricted. Therefore, throughout this study, the classifier’s true-positive rate is equivalent to the classifier’s recall rate.
 
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Metadaten
Titel
Friend or foe? Fake profile identification in online social networks
verfasst von
Michael Fire
Dima Kagan
Aviad Elyashar
Yuval Elovici
Publikationsdatum
01.12.2014
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2014
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-014-0194-4

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