1. Privacy and trust in social network
Michelle Hong 2009/03/02

2. Outline What is privacy and trust? Privacy in social network
Basic privacy requirement
Privacy in graph
Trust in social network
Reference

3. What is Privacy
Privacy is the ability of an individual or group to seclude themselves or information about themselves and thereby reveal themselves selectively.
Different privacy boundaries and content
Voluntarily sacrificed
Uniquely identifiable data relating to a person or persons

4. What is Trust? Trust is a relationship of reliance.
Not related to good character, or morals
Trust does not need to include an action that you and the other party are mutually engaged in.
Trust is a prediction of reliance on an action.
Conditional

5. Privacy and Trust Tradeoff
Need legal rights
Reveal more data to trustworthy people
Provide access rights
Gain trust through open sensitive data

6. Outline What is privacy and trust? Privacy in social network
Basic privacy requirement
Privacy in graph
Trust in social network
Reference

7. K-anonymous [1] Have at least k answers Given multiple data publisher
Get sensitive value
Have at least k answers

8. L-diversity [2]
Have at least l different sensitive answers

9. t-closeness [3]
T semantic meaning result

10. Dynamic Anonymization [4]

11. Outline What is privacy and trust? Privacy in social network
Basic privacy requirement
Privacy in graph
Trust in social network
Reference

12. Possible Attacks On Anonymized Graphs
Attack method [5]
Identify by neighborhood information
It includes:
Vertex Refinement Queries
Sub-graph Queries
Hub Fingerprint Queries
Attack types[6]
Active Attacks
Create a small number of new user accounts linking with other users before the anonymized graph is generated
Passive Attacks
Indentify themselves in the published graph
Semi-passive Attacks
Create necessary link with other users

13. Vertex Refinement Queries
H*’s computation is linear in the number of edges in the graph, very efficiently.

14. Sub-graph Queries
Query is the subgraph information adjacent to the target node
Computation intensive

15. Hub Fingerprint Queries
Suppose Dave and Ed are selected as hubs
F1(fred) = (1, 0) (The shortest path length to each hub)
F2(fred) = (1, 2)
If F1(fred) = (1, 0) in open world, then both F1(fred) = (1, 0) and (1, 1) are candidate because the adversary may not have the complete knowledge

16. Avoid attacks Request authorities to linkage confirmation
Users confirm a request about adding a friend
Website provides checking on users
Identify and remove attack nodes
Find the strange structure nodes

17. k-degree anonymous[7] The kind of attack Objective Method
Vertex Refinement Queries (H(1))
Objective
The published graph
For every node v, there exist at least k-1 other nodes in the graph with the same degree as v
Minimum edges are added in to reserve the graph’s shape as much as possible
Method
Add edges into the original anonymized graph
First compute the new degree vector that satisfy k-degree
Then generate the new graph based on this degree vector

18. K-neighbor anonymous [8]

19. Resist neighborhood attack through graph generalization[5]2
Step1: Partition the graph, each partition contains at least k nodes 1 1 3 2 2 2
Step2: For each partition, generate a super node 2 3 3 2
Step3: Draw the edges between partitions, the weight is the edge number
In this paper, he use simulated annealing to find the partitions maximize the likelihood function
Step3: Draw the sel-edges for each partition, the weight is the edge number with it

20. Outline What is privacy and trust? Privacy in social network
Basic privacy requirement
Privacy in graph
Trust in social network
Reference

21. Mining Privacy in Social Network
What’s the problem in Web 2.0: 
Activity streams: users are not aware that some mini-feeds on the profile
Unwelcome linkage: a friend who explicitly write the link for other user's profile
merge social graph: link of link

22. Privacy in Social Data
Different users have different opinions on sensitive data
Website enables users to set up access permission
Construct trust network from social data

23. Reference
[1] L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; [2] Ashwin Machanavajjhala , Daniel Kifer , Johannes Gehrke , Muthuramakrishnan Venkitasubramaniam, L-diversity: Privacy beyond k-anonymity, ACM Transactions on Knowledge Discovery from Data (TKDD), v.1 n.1, p.3-es, March 2007 [3] Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian, "t-Closeness: Privacy Beyond k- Anonymity and l-Diversity," in IEEE International Conference on Data Engineering (this proceedings), [4] Xiao, X., Tao, Y. Dynamic Anonymization: Accurate Statistical Analysis with Privacy Preservation. Proceedings of ACM Conference on Management of Data (SIGMOD), pages , [5] Michael Hay, Gerome Miklau, David Jensen, Don Towsley and Philipp Weis, Resisting Structural Re-identification in Anonymized Social Networks. PVLDB08 [6] Lars Backstrom, Cynthia Dwork and Jon Kleinberg, Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography. WWW2007 [7] Kun liu and Evimaria Terzi, Towards Identity Anonymization on Graphs. SIGMOD08 [8] Bin Zhou and Jian Pei, Preserving Privacy in Social Networks Against Neighborhood Attacks ICDE08