Presentation is loading. Please wait.

Presentation is loading. Please wait.

Introduction Graph Data Why data sharing/publishing

Published byIris Preston Modified over 8 years ago

Similar presentations

Presentation on theme: "Introduction Graph Data Why data sharing/publishing"— Presentation transcript:

1

2 Introduction Graph Data Why data sharing/publishing
Social networks data Mobility traces Medical data …… Why data sharing/publishing Academic research Business applications Government applications Healthcare applications

3 Anonymization and De-anonymization
Utility Anonymization vs DA

4 Contribution We studied and analyzed
11 state-of-the-art graph anonymization techniques 15 modern Structure-based De-Anonymization (SDA) attacks We developed and evaluated SecGraph 11 anonymization techniques 12 graph utility metrics 7 application utility metrics 15 de-anonymization attacks We released SecGraph Publicly available at

5 Outline Introduction Graph Anonymization Graph De-anonymization
Anonymization vs. De-anonymization Analysis SecGraph Future Research and Challenges Conclusion

6 Graph Anonymization Naïve ID Removal
Edge Editing (EE) based anonymization Add/Del Switch K-anonymity K-Neighborhood Anonymization (k-NA) K-Degree Anonymization (k-DA) K-automorphism (k-auto) K-isomorphism (k-iso) Aggregation/class/cluster based anonymization Differential Privacy (DP) based anonymization Random Walk (RW) based anonymization

7 Utility Metrics Graph utility (12) Application utility (7)
Degree (Deg.) Joint Degree (JD) Effective Diameter (ED) Path Length (PL) Local Clustering Coefficient (LCC) Global Clustering Coefficient (GCC) Closeness Centrality (CC) Betweenness Centraltiy (BC) Eigenvector (EV) Network Constraint (NC) Network Resilience Infectiousness (Infe.) Application utility (7) Role eXtraction (RX) Influence Maximization (IM) Minimum-sized Influential Node Set (MINS) Community Detection (CD) Secure Routing (SR) Sybil Detection (SD) Graph utility captures how the anonymized data preserve fundamental structural properties of the original graph How useful the anonymized data are for practical graph applications and mining tasks

8 Anonymization vs Utility
Anonymization Techniques preserve the utility Resilient to DA attacks Conditionally preserve the utility Not preserve the utility Partially preserve the utility

9 Anonymization vs Utility
Anonymization Techniques Resilient to DA attacks Conditionally preserve the utility Not preserve the utility Partially preserve the utility

10 Outline Introduction Graph Anonymization Graph De-anonymization
Anonymization vs. De-anonymization Analysis SecGraph Future Research and Challenges Conclusion

11 Graph De-Anonymization (DA)
Seed-based DA Backstrom et al.’s attack (BDK) Narayanan-Shmatikov’s attack (NS) Narayanan et al.’s attack (NSR) Nilizadeh et al.’s attack (NKA) Distance Vector (Srivatsa-Hicks attack) (DV) Randomized Spanning Trees (Srivatsa-Hicks attack) (RST) Recursive Subgraph Matching (Srivatsa-Hicks attack) (RSM) Yartseva-Grossglauser’s attack (YG) De-Anonymization attack (Ji et al.’s attack) (DeA) Adaptive De-Anonymization attack (Ji et al.’s attack) (ADA) Korula-Lattanzi’s attack (KL) Seed-free DA Pedarsani et al.’s attack (PFG) Ji et al.’s attack (JLSB) Seed mappings Anonymized Graph Auxiliary Graph Figure source: E. Kazemi et al., Growing a Graph Matching from a Handful of Seeds, VLDB 2015

12 DA Attacks SF: seed-free AGF: auxiliary graph-free
SemF: semantics-free A/P: active/passive attack Scal.: scalable Prac.: practical Rob.: robust to noise

13 Outline Introduction Graph Anonymization Graph De-anonymization
Anonymization vs. De-anonymization Analysis SecGraph Future Research and Challenges Conclusion

14 State-of-the-art Anonymization Techniques
Anonymization vs. DA State-of-the-art Anonymization Techniques : vulnerable : conditionally vulnerable : invulnerable Modern DA Attacks DA attacks!

15 Outline Introduction Graph Anonymization Graph De-anonymization
Anonymization vs. De-anonymization Analysis SecGraph Future Research and Challenges Conclusion

16 SecGraph: Overview & Implementation
11 Anonymization Techniques 19 Utility Metrics 15 DA Attacks Raw Data -> AM -> Anonymized Data -> UM/DM -> Publishing Data Raw Data -> Publishing Data Raw Data -> UM/DM -> AM -> Anonymized Data -> Publishing Data

17 SecGraph: Evaluation Datasets Evaluation settings Evaluation scenarios
Enron (36.7K users, 0.2M edges): an network dataset Facebook – New Orleans (63.7K users, 0.82M edges): a Facebook friendship dataset in the New Orleans area Evaluation settings Follow the same/similar settings in the original papers See details in the paper and Evaluation scenarios Anonymization vs Utility DA performance Robustness of DA attacks Anonymization vs DA

18 SecGraph: Anonymity vs Utility
C1: existing anonymization techniques are better at preserving graph utilities C2: all the anonymization techniques lose one or more application utility

19 SecGraph: DA Performance
Phase transitional phenomena C1: the phase transitional (percolation) phenomena is attack-dependent

20 SecGraph: Robustness of DA Attacks
% of error seeds C1: global structure-based DA attacks are more robust to seed errors

21 SecGraph: Anonymization vs DA
C1: state-of-the-art anonymization techniques are vulnerable to one or several modern DA attacks C2: no DA attack is general optimal. The DA performance depends on several factors, e.g., similarity between anonymized and auxiliary graphs, graph density, and DA heuristics

22 SecGraph: Release & Support
Website Software Datasets Documents Demo Q&A Modes GUI Command line Supporting Windows Linux Mac

23 Outline Introduction Graph Anonymization Graph De-anonymization
Anonymization vs. De-anonymization Analysis SecGraph Future Research and Challenges Conclusion

24 Future Research and Challenges
Graph data anonymization Application-oriented anonymization techniques DA Combine the advantages of different attacks Anonymization technique-oriented and application-aware DA attacks More future work De-anonymizability quantification Anonymity-utility tradeoff Support multiple data types …… Utility Anonymity

25 Conclusion Acknowledgement
We analyzed existing graph anonymization techniques and de-anonymization attacks We implemented and evaluated SecGraph: a uniform and open-source evaluation system from graph data anonymization and de-anonymization Acknowledgement We thank the anonymous reviewers very much for their valuable comments! We are grateful to the following researchers in developing SecGraph: Ada Fu, Michael Hay, Davide Proserpio, Qian Xiao, Shirin Nilizadeh, Jing S. He, Wei Chen, and Stanford SNAP developers

26 Thank you! Shouling Ji sji@gatech.edu

Download ppt "Introduction Graph Data Why data sharing/publishing"

Similar presentations

Supporting Cooperative Caching in Disruption Tolerant Networks

Supporting Cooperative Caching in Disruption Tolerant Networks
Private Analysis of Graph Structure With Vishesh Karwa, Sofya Raskhodnikova and Adam Smith Pennsylvania State University Grigory Yaroslavtsev

Private Analysis of Graph Structure With Vishesh Karwa, Sofya Raskhodnikova and Adam Smith Pennsylvania State University Grigory Yaroslavtsev
Automating Graph-Based Motion Synthesis Lucas Kovar Michael Gleicher University of Wisconsin-Madison.

Automating Graph-Based Motion Synthesis Lucas Kovar Michael Gleicher University of Wisconsin-Madison.
1 Social Influence Analysis in Large-scale Networks Jie Tang 1, Jimeng Sun 2, Chi Wang 1, and Zi Yang 1 1 Dept. of Computer Science and Technology Tsinghua.

1 Social Influence Analysis in Large-scale Networks Jie Tang 1, Jimeng Sun 2, Chi Wang 1, and Zi Yang 1 1 Dept. of Computer Science and Technology Tsinghua.
Structural Data De-anonymization: Quantification, Practice, and Implications Shouling Ji, Weiqing Li, and Raheem Beyah Georgia Institute of Technology.

Structural Data De-anonymization: Quantification, Practice, and Implications Shouling Ji, Weiqing Li, and Raheem Beyah Georgia Institute of Technology.
Xiaowei Ying Xintao Wu Univ. of North Carolina at Charlotte 2009 SIAM Conference on Data Mining, May 1, Sparks, Nevada Graph Generation with Prescribed.

Xiaowei Ying Xintao Wu Univ. of North Carolina at Charlotte 2009 SIAM Conference on Data Mining, May 1, Sparks, Nevada Graph Generation with Prescribed.
Leting Wu Xiaowei Ying, Xintao Wu Dept. Software and Information Systems Univ. of N.C. – Charlotte Reconstruction from Randomized Graph via Low Rank Approximation.

Leting Wu Xiaowei Ying, Xintao Wu Dept. Software and Information Systems Univ. of N.C. – Charlotte Reconstruction from Randomized Graph via Low Rank Approximation.
Localized Techniques for Power Minimization and Information Gathering in Sensor Networks EE249 Final Presentation David Tong Nguyen Abhijit Davare Mentor:

Localized Techniques for Power Minimization and Information Gathering in Sensor Networks EE249 Final Presentation David Tong Nguyen Abhijit Davare Mentor:
Efficient Hop ID based Routing for Sparse Ad Hoc Networks Yao Zhao 1, Bo Li 2, Qian Zhang 2, Yan Chen 1, Wenwu Zhu 3 1 Lab for Internet & Security Technology,

Efficient Hop ID based Routing for Sparse Ad Hoc Networks Yao Zhao 1, Bo Li 2, Qian Zhang 2, Yan Chen 1, Wenwu Zhu 3 1 Lab for Internet & Security Technology,
1 Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles The 3rd ACM Conference on Recommender Systems, New.

1 Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles The 3rd ACM Conference on Recommender Systems, New.
Suppose I learn that Garth has 3 friends. Then I know he must be one of {v 1,v 2,v 3 } in Figure 1 above. If I also learn the degrees of his neighbors,

Suppose I learn that Garth has 3 friends. Then I know he must be one of {v 1,v 2,v 3 } in Figure 1 above. If I also learn the degrees of his neighbors,
Malicious parties may employ (a) structure-based or (b) label-based attacks to re-identify users and thus learn sensitive information about their rating.

Malicious parties may employ (a) structure-based or (b) label-based attacks to re-identify users and thus learn sensitive information about their rating.
April 13, 2010 Towards Publishing Recommendation Data With Predictive Anonymization Chih-Cheng Chang †, Brian Thompson †, Hui Wang ‡, Danfeng Yao † †‡

April 13, 2010 Towards Publishing Recommendation Data With Predictive Anonymization Chih-Cheng Chang †, Brian Thompson †, Hui Wang ‡, Danfeng Yao † †‡
2. Attacks on Anonymized Social Networks. Setting A social network Edges may be private –E.g., “communication graph” The study of social structure by.

2. Attacks on Anonymized Social Networks. Setting A social network Edges may be private –E.g., “communication graph” The study of social structure by.
The Union-Split Algorithm and Cluster-Based Anonymization of Social Networks Brian Thompson Danfeng Yao Rutgers University Dept. of Computer Science Piscataway,

The Union-Split Algorithm and Cluster-Based Anonymization of Social Networks Brian Thompson Danfeng Yao Rutgers University Dept. of Computer Science Piscataway,
Privacy Preservation for Data Streams Feifei Li, Boston University Joint work with: Jimeng Sun (CMU), Spiros Papadimitriou, George A. Mihaila and Ioana.

Privacy Preservation for Data Streams Feifei Li, Boston University Joint work with: Jimeng Sun (CMU), Spiros Papadimitriou, George A. Mihaila and Ioana.
Structure based Data De-anonymization of Social Networks and Mobility Traces Shouling Ji, Weiqing Li, and Raheem Beyah Georgia Institute of Technology.

Structure based Data De-anonymization of Social Networks and Mobility Traces Shouling Ji, Weiqing Li, and Raheem Beyah Georgia Institute of Technology.
Right Protection via Watermarking with Provable Preservation of Distance-based Mining Spyros Zoumpoulis Joint work with Michalis Vlachos, Nick Freris and.

Right Protection via Watermarking with Provable Preservation of Distance-based Mining Spyros Zoumpoulis Joint work with Michalis Vlachos, Nick Freris and.
Midterm Presentation Undergraduate Researchers: Graduate Student Mentor: Faculty Mentor: Jordan Cowart, Katie Allmeroth Krist Culmer Dr. Wenjun (Kevin)

Midterm Presentation Undergraduate Researchers: Graduate Student Mentor: Faculty Mentor: Jordan Cowart, Katie Allmeroth Krist Culmer Dr. Wenjun (Kevin)
Learning Table Extraction from Examples Ashwin Tengli, Yiming Yang and Nian Li Ma School of Computer Science Carnegie Mellon University Coling 04.

Learning Table Extraction from Examples Ashwin Tengli, Yiming Yang and Nian Li Ma School of Computer Science Carnegie Mellon University Coling 04.

Similar presentations

Ads by Google