ppdm in social network 2nd part
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Transcript of ppdm in social network 2nd part
Privacy Preserving Social
Network Data Mining(2nd) S.HAMIDE RASOULI
23.8.1394
ALZAHRA UNIVERSITY
ADVANCED TOPICS IN SOFTWARE ENGINEERING/ DR.KEYVANPOUR
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INDEX
Review
ML in Classical approaches
Drawbacks of all classical approaches
ML approach
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Review/why
3
Background knowledge
Identifying attributes of
vertices
Vertex degree
Link Relationship
Neighborhoods
Embedded subgraphs
Graph Metrics
Mutual Friend Attack
Neighborhood Attack
Friendship Attack
Degree Attack
/19
Review/what
Privacy in Social Network
Vertex existence (milunair network)
Vertex property (degree , distance …)
Sensitive Vertex Label
Link Relationship (Financial)
Link Weight
Sensitive Edge Labels
Graph Metrics (general graph
properties, aggregate network
querries)
4
Privacy vs Utility
Data
Identifier
Quasi Identifier
Sensitive (privacy)
Not sensitive (Utility)
/19
Review/How
Sanitization/Modification operation approaches
Suppression ?, ** or simply delete
Generalization
Adding Noise (adding or altering artificial edge , node & content)
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PPDM vs ML
6
ML in Attacks
ML in Protections
/19
Classical approaches
7
K-anonymity
/19
ML in Clustering approach
Super Node/Super Edge
Publish Coarse Resolution
Privacy Level Parameter = Cluster Size
8
Node Clustering
Clustering Methods
Node
Edge
Node & Edge
Vertex-Attribute mapping
Node & Edge Clustering
/19
ML in Random Based Anonymization
approach
9
Not adding or removing any edges(adding noise) cost the same
Goal : to Preserve Utility
Social Role
/19
ML in K-Anonymity
Goal : to Preserve Utility
Special background knowledge
K-anonymity/L-diversity/T-closeness
10
age Similarity
(No lable) CLUSTERING
4-Anonymity
Age : QI
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ML in K-Anonymity
Age : QI
Disease : Sensitive
11
age
4-Anonymity
K-anonymity L-diversity T-closeness
Ali has a hard
disease
Ali has
cancer Ali has ?
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Clustering + Modifications
12 /19
Another type of ML usage in ppsndm
APA (attack-protect-attack)
Naïve baysian classifier:
Local classifier (Prior label)
Relational classifier(Hemophily Rule) (Update Label)
Collaborative inference algorithm (Iteration)
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Drawbacks of all these approaches
Handy (parameter …)
Modifications Not Generic
Special Attack Privacy
Special measure Utility
Clustering Utility Loss(local knowledge)
a set of Anonymization procedures
14 /19
ML approach/1
15
Automatic
Generic
Optimization(utility/privacy tradeoff)
∆(g,g') , R(g,g')
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ML approach/2
16 /19
Other Solutions
Decenteralization Distribution
Some Hiuristics
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REFRENCES: 1-GRAPH ANONYMIZATION USING MACHINE LEARNING/MARIA LAURA MAAG, LUDOVIC DENOYER, PATRICK GALLINARI/2014 IEEE 28TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONSEMPIRICAL
2-COMPARISONS OF ATTACK AND PROTECTION ALGORITHMSFOR ONLINE SOCIAL NETWORKS/MINGZHEN MO, IRWIN KINGA, AND KWONG-SAK LEUNG PROCEDIA COMPUTER SCIENCE 5 (2011)THE 8TH INTERNATIONAL CONFERENCE ON MOBILE WEB INFORMATION SYSTEMS (MOBIWIS)
اولینهمایشملیکاربردسیستم/عبدالهیازگمیمحمد,احسانسرگلزایی/اجتماعیههایارائهالگوریتمیحریصانهبرایحفظحریمخصوصیدادههایمنتشرشدهشبک-3
قوچاندرعلوموصنایعدانشگاهآزاداسالمیواحد(محاسباتنرم)هایهوشمند
4-ATTACK VECTOR ANALYSIS AND PRIVACY-PRESERVING SOCIAL NETWORK DATA PUBLISHING/MOHD IZUAN HAFEZ NINGGAL JEMAL ABAWAJY/2011 INTERNATIONAL JOINT CONFERENCE OF IEEE TRUSTCOM-11/IEEE ICESS-11/FCST-11
5-EDGE ANONYMITY IN SOCIAL NETWORK GRAPHS/LIJIE ZHANG AND WEINING ZHANG/2009 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING
6-ANONYMIZATION OF CENTRALIZED AND DISTRIBUTED SOCIAL NETWORKS BY SEQUENTIAL CLUSTERING/TAMIR TASSA
AND DROR J. COHEN/IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2013
7-A HYBRID ALGORITHM FOR PRIVACY PRESERVING SOCIAL NETWORK PUBLICATION PENG LIU, LEI CUI1, AND XIANXIAN LI SPRINGER 2014
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Thank you all
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