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Retrieving Private Information from SNS’s Users on the Internet

Last week, E-Crime Expert started a series that aims to contribute to a better understanding regarding why privacy and personal data are so vulnerable in relation to the Internet and its adjacent services/platforms.

Today, E-Crime Expert shows how Users’ private information can be retrieved.

Regarding users’ private information, according to Ron Bowes, a security consultant and blogger at Skull Security, anyone could download a Facebook user’s profile from the Facebook public profile directory. Ron Bowes, as a demo for how personal information could be retrieved from Facebook by employing data mining techniques, performed the following operation. He downloaded 171 million public profiles (including names and email addresses) from Facebook out of 500 million registered users. After this, he compiled all this data in a file, which he uploaded on The Pirate Bay torrent. From The Pirate Bay 2, 923 users (seeds) had downloaded the file by July 29, 2010, the date the article was written.

Fig. 1 List of top users

Fig. 2 List of users’ first name

Fig. 3 List of users’ first name and last name

Fig. 4 The torrent

Bernie Hogan, research fellow at Oxford Internet Institute employed another experiment, using NodeXL, aiming to prove that someone could download from Facebook entire networks related to a topic, group or entity. “NodeXL is a template for Excel 2007 and 2010 that lets you enter a network edge list, click a button, and see the network graph, all in the Excel window”.

Credits and copyright: Bernie Hogan, Research Fellow at Oxford Internet Institute.

Different networks represent different social worlds of users. In the network graphics, one could see the most connections a user makes with other users and the relationship among these users by reading the connection strength between the nod (central user) and his “friend’s list”. If one exchanges a few messages with another, the shown graph will be small, but if a user heavily exchanges messages with another, the graph will be high. The media’s usage of rapports between users could also be established by using NodeXL. To monetized that information downloaded with NodeXL it becomes easier as it refers to particular groups where the members share the same preferences in terms of products, services, publications, beliefs, etc. By establishing which group someone joins (with its particular themes or topics), it could be easily determined what kind of preferences a particular user has.

Credits and copyright: Bernie Hogan, Research Fellow at Oxford Internet Institute.

 User preferences could be gained very easily through methods such as: the use of NodeXL to import various data stored on multiple formats including GraphML, UCINet, Pajek, and matrix. It could connect directly to Twitter and YouTube allowing the import of the network of users that “tweeted” on a certain topic or keyword. If one uses Email clients (Outlook, Windows Mail, etc), NodeXL could import the network to whom the user communicated in the past days or weeks. NodeXL features among others, permits to use an Auto filling option when the program is started in order to “command” the program on how much information to collect, from how many users, how the graph would be.

Credits and copyright: Bernie Hogan, Research Fellow at Oxford Internet Institute.

Analyzing the relationship and connections between Facebook users and their friends, could determine communities of a particular user, even outside of the SNS, including real life information such as: the university, college, doctor, club, etc. to whom the users is affiliated or has graduated over time. Because if user X is connected to user Y just because they were colleagues in college, this is how someone’s education could be reveled to a third party, without his acknowledgment. The same algorithm applies when YouTube videos are uploaded or watched. If one maps the links between videos and users, they could determine which are the most connected users, who is the most prolific content creator and also the most active commentator.

To conclude, by employing social network analysis (SNA) through systems such as NodeXL, for mapping connections and relations among users in a social network, key people, groups, activities, and changes, the core of that particular network could be determined, which could lead to more private information. Also, by knowing the “leader” of a group or community, it could enable someone to take positive or negative actions against that person which has “authority” among her community.

Both online and off, people frequently perform particular social roles. These roles organize behavior and give structure to positions in local networks. As more of social life becomes embedded in online systems, the concept of social role becomes increasingly valuable as a tool for simplifying patterns of action, recognizing distinct user types, and cultivating and managing communities.”

Finally, the interests of a group and its preferences could be determined and could support the offering of mass advertising or marketing, profiling or behavioral advertising.

The question here is why should someone have access to a user’s preferences and social rapports with other users and be able to view the frequency of communications and social activity?

Any questions can be submitted to: dan@e-crimeexpert.com

Additional information can be found at: www.e-crimeexppert.com

Did you know about this? Did you use these methods? 

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