DNE: A Method for Extracting Cascaded Diffusion Networks from Social Networks
A nonymized social networks
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Transcript of A nonymized social networks
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Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Stenography
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A social network occurs anywhere there is social interaction between people.
Examples include Email, instant messaging, Facebook, blogging trackbacks, coauthor networks
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The structure of social networks can be interesting
How are friendships usually structured? Are there hubs, such as Heather, who connect separate networks? How many degrees of Kevin Bacon?
We can investigate these questions if we have the data to mine.
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For our examples, we will use a network of emails sent between users.
How do we protect users’ privacy while still releasing the data for research?
John Mary
Vertex
Vertex
Directed edge
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Remove any identifiable information, such as name and other attributes.
Randomly rename the vertices
R3579X R73313
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Convert directed edges to undirected edges. This increases the complexity and makes it harder to attack.
R3579X R73313
Undirected edge
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Let’s say you want to know if two vertices are connected onthe graph.
All the identifying info has beenremoved, so how do we do it?
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An active attack involves the adversary creating vertices in the graph before the graph is released
The adversary will create edges between the vertices in a fashion that it can then recognize later on in when the graph is released
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We create k new vertices around 2*(log n) where n is the total number of vertices
We create new do – d1 edges between these new vertices and the other ones in the graph
Then, we randomly create edges between these new nodes with independent probability of 1/2
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Given the graph, how do we find the subgraph that we created?
Create a search tree, pruning the tree based on the properties of our subgraph, such as the number of degrees of our new vertices
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Tom
John
Mary
Mike
Zoe
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Tom
John
Mary
k5 k1
k2k4
k3
Mike
Zoe
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Tom
John
Mary
k5 k1
k2k4
k3
Mike
Zoe
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Tom
John
Mary
k5 k1
k2k4
k3
Mike
Zoe
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JKL
ZXCV
QWER
DFGWER
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HGF
ASDF
BNM
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JKL
ZXCV
QWER
k5 k1
k2k4
k3
ASDF
BNM
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JKL
John
Mary
k5 k1
k2k4
k3
ASDF
BNM
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The paper proves that the search tree does not grow too large and that the algorithm displays good performance
Also, it proves that the subgraph is unique so that we don’t identify the wrong subgraph
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They simulate an attack on LiveJournal friendship links. They create the accounts on the website, make the connections, and then crawl the site and anonymize the data
The network has 4.4 million nodes and 77 million edges
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Only needs sqrt(log(n)) new nodes to attack the graph
However, it’s much more computationally intensive and less practical in the real world, although it takes less nodes
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It’s a lot like an active attack, except you don’t create new nodes, instead you collaborate with your friends and find yourselves in the graph
However, because you did not specifically target certain people, you may not be able to identify other people when you find yourself
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We cannot rely on anonymization to ensure privacy in social networks
Possible improvements: add noise to the data by adding/removing random edges