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

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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)

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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

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Classical approaches

7

K-anonymity

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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

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ML in Random Based Anonymization

approach

9

Not adding or removing any edges(adding noise) cost the same

Goal : to Preserve Utility

Social Role

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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

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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

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ML approach/1

15

Automatic

Generic

Optimization(utility/privacy tradeoff)

∆(g,g') , R(g,g')

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ML approach/2

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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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