privacy and trust in social network michelle hong 2009/03/02
TRANSCRIPT
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PRIVACY AND TRUSTIN SOCIAL NETWORK
Michelle Hong2009/03/02
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Outline
What is privacy and trust? Privacy in social network
Basic privacy requirement Privacy in graph
Trust in social network Reference
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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
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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
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Privacy and Trust Tradeoff
Need legal rights Reveal more data to
trustworthy people
Provide access rights Gain trust through
open sensitive data
Privacy Trust
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Outline
What is privacy and trust? Privacy in social network
Basic privacy requirement Privacy in graph
Trust in social network Reference
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K-anonymous [1]
Given multiple data publisher Get sensitive value
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L-diversity [2]
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t-closeness [3]
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Dynamic Anonymization [4]
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Outline
What is privacy and trust? Privacy in social network
Basic privacy requirement Privacy in graph
Trust in social network Reference
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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
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Vertex Refinement Queries
H*’s computation is linear in the number of edges in the graph, very efficiently.
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Sub-graph Queries
Query is the subgraph information adjacent to the target node
Computation intensive
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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
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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
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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
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K-neighbor anonymous [8]
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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
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Outline
What is privacy and trust? Privacy in social network
Basic privacy requirement Privacy in graph
Trust in social network Reference
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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
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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
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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