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Personalized Privacy Protection in Social Networks

Personalized Privacy Protection in Social Networks. Speaker : XuBo. Privacy Protection. Privacy Protection in Social Networks. more and more people join multiple social networks on the Web

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Personalized Privacy Protection in Social Networks

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  1. Personalized Privacy Protection in Social Networks Speaker : XuBo

  2. Privacy Protection

  3. Privacy Protection in Social Networks • more and more people join multiple social networks on the Web • as a service provider, it is essential to protect users’ privacy and at the same time provide “useful”data

  4. Previous Works • Clustering-based approaches • Graph editing methods • Drawback • Different users may have different privacy preferences • Same level may not be fair and data may useless

  5. Personalized Privacy Protection in Social Networks • define different levels of protection for users and incorporate them into the same published social network • A user can have a clear estimation of the knowledge that an attacker can know about him • The knowledge an attacker uses to find the privacy information of a user is called the background knowledge

  6. background knowledge

  7. A simple example • protection objectives • the probability that an attacker finds a person P is node u in the published graph should be less than 50% • the probability that an attacker finds person P1 and P2 have a connection is less than 50%

  8. Personalized Protection

  9. Method • For Level 1 protection, we use node label generalization • For Level 2 protection, we combine the noise node/edge adding methods based on the protection at Level 1 • For Level 3 protection, we further use the edge label generalization to achieve the protection objectives

  10. Abstract • PROBLEM DEFINITION • LEVEL 1 PROTECTION • LEVEL 2 PROTECTION • LEVEL 3 PROTECTION • EXPERIMENTS

  11. PROBLEM DEFINITION • G(V,E), Given a constant k

  12. NP-hard problem • Here the cost stands for the sum of label generalization levels. For example, in Figure 1(d), if let the labels in the group {a,d} to be the same, they become [(Africa, American), 2*], thus the node label generalization cost of this group is 4.

  13. LEVEL 1 PROTECTION • Level 1’s background knowledge is about node label list • generalization is to group nodes and make the nodes within each group have one generalized node label list

  14. Achieve Level 1 protection • if each group’s size is at least k, the first objective is achieved. • To satisfy objectives (2) and (3), the following condition must be satisfied: The first condition constrains that any two nodes in the same group do not connect to a same node The second condition constrains that no edges within a group

  15. The Safety Grouping Condition (SGC)

  16. Algorithm 1: Generate safe groups

  17. LEVEL 2 PROTECTION • Adding nodes/edges into GL1 • Label Assignment

  18. Construct a k-degree anonymous graph • The first step is to set a target degree for each node • the second step is to randomly create edges between the nodes which need to increase their degrees under SGC • we add noise nodes to enable all the nodes in GL1 have their target degrees by connecting them with noise nodes under SGC. • we need to hide the noise nodes by making their degree to a preselected value degree target Degree target is the degree that the maximum number of groups of size k in GL1 have

  19. Algorithm 2: Degree Anonymizing Algorithm with X

  20. A running example of Algorithm 2

  21. Label Assignment • Nocon(A1,A2, l) • a node/edge label assignment with less Nocon(A1,A2, l) changes is preferred • heuristic method • first assign labels to the noise edges • decide the labels of noise nodes

  22. Assign labels to the noise edges

  23. Decide the labels of noise nodes

  24. LEVEL 3 PROTECTION • degree label sequence • a group contains at least one node that needs Level 3 protection, for any nodes in it, change their degree label sequence to be the same by generalizing edge labels.

  25. Generate degree label anonymous graph

  26. EXPERIMENTS • Utilities • One hop query (1 hop) • (l1, le, l2) • Two hop query (2 hop) • (l1, le, l2, le2, l3) • Relative error = • Degree Distribution • EMD (Earth Mover Distance) • a good measure of the difference between two distributions

  27. Datasets • Speed Dating Data • 552 nodes and 4194 edges • Each node has 19 labels • Edge label is “match”/”unmatch” • ArXivData • 19835 nodes and 40221 edges • Each node denotes an author • set label ”seldom” to edges with weight 1, ”normal” to edges with weight 2 - 5 and ”frequent” to edges with weight larger than 5 • ArXivData with uniform labels

  28. Results • higher utility • average relative error increases with the increasing of k

  29. Results(2)

  30. Results(3)

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