PRIVACY AND TRUSTIN SOCIAL NETWORK

Michelle Hong2009/03/02

Outline

What is privacy and trust? Privacy in social network

Basic privacy requirement Privacy in graph

Trust in social network Reference

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

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

Privacy and Trust Tradeoff

Need legal rights Reveal more data to

trustworthy people

Provide access rights Gain trust through

open sensitive data

Privacy Trust

Outline

What is privacy and trust? Privacy in social network

Basic privacy requirement Privacy in graph

Trust in social network Reference

K-anonymous [1]

Given multiple data publisher Get sensitive value

L-diversity [2]

t-closeness [3]

Dynamic Anonymization [4]

Outline

What is privacy and trust? Privacy in social network

Basic privacy requirement Privacy in graph

Trust in social network Reference

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

Vertex Refinement Queries

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

Sub-graph Queries

Query is the subgraph information adjacent to the target node

Computation intensive

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

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

k-degree anonymous[7]

The kind of attack 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

K-neighbor anonymous [8]

Resist neighborhood attack through graph generalization[5]

Step1: Partition the graph, each partition

contains at least k nodes

Step2: For each partition, generate a

super node

1

1

2

3

2

3

Step3: Draw the edges between

partitions, the weight is the edge number

3

22

Step3: Draw the sel-edges for each

partition, the weight is the edge number

with it

2

2

In this paper, he use simulated annealing to find the partitions maximize the likelihood function

Outline

What is privacy and trust? Privacy in social network

Basic privacy requirement Privacy in graph

Trust in social network Reference

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

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

Reference

[1] L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 557-570. 

[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), 2007.

[4] Xiao, X., Tao, Y. Dynamic Anonymization: Accurate Statistical Analysis with Privacy Preservation. Proceedings of ACM Conference on Management of Data (SIGMOD), pages 107-120, 2008.

[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