De-anonymizing Social Networks

March 27, 2009
49 Views

Just saw via this article on Techmeme that my friend Vitaly Shmatikov co-authored a paper on “De-anonymizing Social Networks“.

Here’s the abstract as a teaser:

Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers, and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc.

We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized social-network graphs. To demonstrate its effectiveness on real-world networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate.

Our de-anonymization algorithm is based purely on the network topology, does not require creation of a large number of dummy “sybil” nodes, is robust to noise and all existing defenses, and works even when the overlap between the target network and the

Just saw via this article on Techmeme that my friend Vitaly Shmatikov co-authored a paper on “De-anonymizing Social Networks“.

Here’s the abstract as a teaser:

Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers, and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc.

We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized social-network graphs. To demonstrate its effectiveness on real-world networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate.

Our de-anonymization algorithm is based purely on the network topology, does not require creation of a large number of dummy “sybil” nodes, is robust to noise and all existing defenses, and works even when the overlap between the target network and the adversary’s auxiliary information is small.

Link to original post

You may be interested

Education and the Blockchain – Should We be Teaching Blockchain in Schools?
IT
55 shares497 views
IT
55 shares497 views

Education and the Blockchain – Should We be Teaching Blockchain in Schools?

Glen Allard - July 26, 2017

It goes without saying that tech progress is moving at a rapid pace. Futurists point to Moore’s law – the…

5 Effective Strategies for Boosting IoT Security
Internet of Things
79 shares1,306 views
Internet of Things
79 shares1,306 views

5 Effective Strategies for Boosting IoT Security

Ryan Kh - July 25, 2017

With the emergence of IoT devices that are being rolled out from time to time, the serious IoT security issues…

The Future of Healthcare and Big Pharma is in Big Data Analytics
Analytics
633 views
Analytics
633 views

The Future of Healthcare and Big Pharma is in Big Data Analytics

riteshmehta - July 25, 2017

The healthcare industry recognizes that Big Data as and opportunity and a challenge for the whole sector. Nevertheless, systems and…