1. Security and Privacy in Social Networks
Raymond Heatherly
Data Security and Privacy Lab

2. Social Network Privacy (Heatherly et al)
Facebook currently has over 400 million users
Each of these users specify details about themselves
For example:

3. What about details they don’t specify?
So what?
What about details they don’t specify?
In our previous example, what political affiliation does she have?
What about her job?
Two possible reasons:
Forgot
Don’t want people to know

4. Privacy
But can we figure out anyways?
For instance, is there anything our previous example does state that talks about her job?
An activity talks about ‘my classroom’

5. Learning
Consider a social network as a graph, where the vertices are the users in the network, and the edges are friendship links between those users.
Each node has a finite subset of detail types (hometown, birthdate, groups, books, etc.)
Each detail type has a finite number of detail values (books = The Bible, Harry Potter, etc.)

6. We use these properties to construct three different models:
Model Building
We use these properties to construct three different models:
Details Only
Links Only
Average

7. Details Only
Naïve Bayesian classifier (Detail independence)
Builds a raw model based on training data over all details

8. Links Only
Naïve Bayes based
With changes
Weigh friendships based on similarity

9. Average
Calculate Link only and Details only probabilities and average them

10. Collective Inference
When we classify large graphs, the decisions we make at one node transfer through the graph
CI gives us a series of algorithms to assist with handling these transfers
Local Classifier
Relational Classifier
CI Algorithm

11. Preserving Privacy
What happens when data is released?
In what ways can we decrease accuracy of classifiers?
We can add or remove links or details
Consider what additions mean
What about deletions?

12. Our experiments
Performed on data gathered from the DFW network on Facebook in the Spring of 2008
Performing only link or detail deletions
For Details, remove the best identifiers of any classification globally
For Links, remove links to those individuals most like a person

13. Results

15. Access Control in Social Networks (Carminati et al, 2009)
What about access to resources?
For example, photos: Who should control viewers of a photo on Facebook?
Now, on Facebook, the photo uploader has control of the photo’s viewers
A person in the picture can only untag

16. Parental rights over a minor
What if a photo is of a minor child?
How would a parent be able to (reliably) have photos removed or restricted of their children?
What about limiting children’s access to inappropriate videos over a social network?

17. Propose several generic classes of friends:
Friendship Hierarchy
Propose several generic classes of friends:
Friend
Co-Worker
Family
Some classes can have (user-defined) specific sub-classes, such as a Best Friend, a Boss, a Parent, a child, etc.

18. Project motivation
What if we give all people tagged in a photo some say in who can see photos of them?
Additionally, parents of minor children can also have a say in the permissions of photos of their children
Instead of a static access list, what about inferring the authorizations using semantic reasoners?

19. Data Generation
Facebook doesn’t give full set of its data to researchers
Needed to test efficacy of semantic solution using a comparable size of data
Generated 350 million `users’ with their own security policies
Simulated a scale-free network
Generated Between 750,000 and 350 million resources

20. Implementation challenges
Initially, we attempted to do the reasoning on the entire data set.
SweetRules did not update in-memory model of security policies, so gave incorrect responces
Pellet then crashed due to the amount of memory required to perform inference on data set

21. We then decided to partition data
Partitioning
We then decided to partition data
But any single partition would be a cut that would have edges to (at least) one other partition
These would decrease our accuracy
Dynamic partitioning
Owner
Tagged individuals
Requestor

22. Experiment 1 Friendship types: Security policies: Coworker
Friend: with BestFriend sub-type
Family: with Parent/Child sub-type
Security policies:
1. Strict – Only BestFriends and Family can view photos of self and any child; child may not view any videos
2. Casual – Anyone can see photos; no restriction on child
3. ParentStrict – Anyone can see photos of the parent, only family can see photos of child;

23. Discard almost all Link Types
Experiment 2
Discard almost all Link Types
Keep ParentOf/ChildOf
Replace with a Trust value between 1 and 10

24. Maintained all general and specific link types
Experiment 3
Used a hybrid approach
Maintained all general and specific link types
Each friendship also assigned a Trust Value
i.e. A Best Friend with a TV of 6

25. Time (in seconds) for each inference
Results
Average
Low
High
Link-Type only
0.585
0.562
0.611
Trust Value Only
0.612
0.534
0.598
Value/Trust Hybrid
0.731
0.643
0.811
Time (in seconds) for each inference

26. Questions?