privacy preserving data mining in social network

1. 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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2. INDEX
 Review
 ML in Classical approaches
 Drawbacks of all classical approaches
 ML approach
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3. Review/why
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Background knowledge
 Identifying attributes of
vertices
 Vertex degree
 Link Relationship
 Neighborhoods
 Embedded subgraphs
 Graph Metrics
Mutual Friend Attack
Neighborhood Attack
Friendship Attack
Degree Attack
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4. 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)
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Privacy vs Utility
Data
 Identifier
 Quasi Identifier
 Sensitive (privacy)
 Not sensitive (Utility)
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5. Review/How
Sanitization/Modification operation approaches
 Suppression ?, ** or simply delete
 Generalization
 Adding Noise (adding or altering artificial edge , node & content)
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6. PPDM vs ML
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ML in Attacks
ML in Protections
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7. Classical approaches
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K-anonymity
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8. ML in Clustering approach
 Super Node/Super Edge
 Publish Coarse Resolution
 Privacy Level Parameter = Cluster Size
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Node Clustering
Clustering Methods
Node
Edge
Node & Edge
Vertex-Attribute mapping
Node & Edge Clustering
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9. ML in Random Based Anonymization
approach
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 Not adding or removing any edges(adding noise) cost the same
 Goal : to Preserve Utility
 Social Role
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10. ML in K-Anonymity
 Goal : to Preserve Utility
 Special background knowledge
 K-anonymity/L-diversity/T-closeness
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age
Similarity
(No lable) CLUSTERING
4-Anonymity
Age : QI
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11. ML in K-Anonymity
Age : QI
Disease : Sensitive
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age
4-Anonymity
K-anonymity L-diversity T-closeness
Ali has a hard
disease
Ali has
cancer
Ali has ?
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12. Clustering + Modifications
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13. 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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14. 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
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15. ML approach/1
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 Automatic
 Generic
 Optimization(utility/privacy tradeoff)
∆(g,g') , R(g,g')
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16. ML approach/2
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17. Other Solutions
Decenteralization Distribution
Some Hiuristics
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18. 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)
3-‫‫‬‫ارائه‫الگوریتمی‫حریصانه‫برای‫حفظ‫حریم‫خصوصی‫دادههای‫منتشرشده‫شبک‬‫ه‫های‬‫‫‬‫‫‬‫‫‬‫اجتماعی‬‫‫‬/‫‫‬‫احسان‫سرگلزایی‬‫,‫‬‫محمد‫‬‫عبدالهی‫ازگمی‬‫‫‬/‫اولین‫همایش‫ملی‫کاربرد‫سیستم‫‬
‫های‫هوشمند‫‬(‫محاسبات‫نرم‬‫)‫‬‫در‫علوم‫و‫صنایع‫دانشگاه‫آزاد‫اسالمی‫واحد‫‬‫قوچان‬
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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19. Thank you all
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