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Nov 10, 2022 •195 likes •585 views

GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms 1 1 4 2 Xumeng Wang , Wei Chen , Jia-Kai Chou , Chris Bryan , 2 1 1 Huihua Guan , Wenlong Chen , Tianyi Lao , Kwan-Liu Ma 3 1:

  1. GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms 1 1 4 2 Xumeng Wang , Wei Chen , Jia-Kai Chou , Chris Bryan , 2 1 1 Huihua Guan , Wenlong Chen , Tianyi Lao , Kwan-Liu Ma 3 1: Zhejiang University, State Key Lab of CAD&CG 2: University of California, Davis 3: Alibaba Group 4: Arizona State University

  2. Motivation Could you find the professor? E Anonymize A A C B Prof. D

  3. Structural Features to Identify Nodes • Degree Hub fingerprint # edges connected to a node Hubs A B × √ E Fingerprint • Hub fingerprint Degree = 3 Hub: node with special features A A Fingerprint: connection status with hubs C B B • Subgraph D A group of connected nodes Subgraph (circle)

  4. K-anonymity Structure feature should have at least k occurrences. E Better protection A higher k → A Worse utility C B How to set appropriate k ? D

  5. Motivation Subgraph (cluster) Subgraph (circle) Degree = 1 Degree = 14 Hub fingerprint ( ×√×√ ) Degree = 18 Degree = 10 Subgraph (path) Hub fingerprint ( √√√√ ) Hub fingerprint ( √√×√ ) How to set k for so many features?

  6. Motivation Privacy Experts Visualization Tools Identify privacy issues Intuitive representations Customize schemes Explanation Evaluate results Assessment and comparison

  7. Related Work: Privacy Preservation for Graphs • K-anonymity [ACM SIGMOD 2008, VLDB 2009, ACM SIGMOD 2010] Construct similar (structural) features. • Differential Privacy [ACM SIGKDD 2014, ACM SIGCOMM 2011] Make perturbations to data. • Graph-only Models [ASIACCS 2009, SDM 2008] Cluster nodes or randomly edit edges.

  8. Related Work: Evaluating Privacy Preservation • Privacy preservation • Query results of specific features [VLDB 2008, VLDB 2014] • Utility loss • Structure properties [AJS1987, AJS2004] • Specific analysis tasks [ACM SIGKDD 2012, ACM WSDM 2013]

  9. Related Work: Privacy-aware Visualizations Graph Data [IEEE PVIS 2017] Multi-attribute Tabular Data [IEEE TVCG 2018]

  10. Task Requirements TR1: Learn the characteristics. TR2: Guide auto-processing. TR3: Evaluate and compare schemes. TR4: Record the provenance.

  11. Workflow&Interface Original data Visual specification Privacy preservation Processed data

  12. Workflow Learn About the Characteristics. (TR1) Original data Visual specification Privacy preservation Overview Processed data Distribution

  13. Workflow Specifying utility metrics. (TR3) Original data Visual specification Privacy preservation Processed data Specifying identity priority. (TR2)

  14. Workflow Original data Prioritize these individuals Visual specification Try not to modify these individuals Privacy preservation Do not handle Processed data these individuals

  15. Visual Design: Priority View 333 284 Other nodes 49 All nodes

  16. Workflow Original data Visual specification Privacy preservation Processed data

  17. Visual Design: Protector View K line Amount of feature occurrences Satisfied Unsatisfied Distribution changes

  18. Visual Design: Degree Protector Degree: # edges connected to a node Amount Degree Degree gap

  19. Visual Design: Hub Fingerprint Protector Hub: node with special features Fingerprint: connection status with hubs Ex. fingerprint: ××√ Disconnected K line The amount of occurrences Connected Hub node

  20. Visual Design: Hub Fingerprint Protector Hub: node with special features Fingerprint: connection status with hubs Number of connected hubs 0 1 2 3

  21. Visual Design: Subgraph Protector Subgraph: a group of connected nodes

  22. Workflow Original data Visual specification 1) Identify risk. 2) Specify scheme. Scheme Privacy Utility Privacy preservation S1 S2 Processed data 3) Compare schemes. 4) Execute scheme. (TR3) (TR4)

  23. Workflow Original data Visual specification Privacy preservation Processed data

  24. Visual Design: Provenance View Metric value changes Edge modifications

  25. Workflow Explain the result. (TR4) Original data Visual specification Privacy preservation Processed data

  26. Case: Facebook Friendship Data • Sub- dataset from “Learning to discover social circles in ego networks.” [NIPS2012] • 333 nodes (users) • 2519 edges (friendships)

  28. Case: Face-to-Face Contacts Dataset • Collected during the exhibition INFECTIOUS • http://konect.uni-koblenz.de/networks/sociopatterns-infectious • 410 nodes (participants) • 2765 edges (conversations lasted over 20 seconds)

  29. Case: Face-to-Face Contacts Dataset Scheme2 Lock: 98%~100% Scheme1 Lock: 0%~2%

  30. Case2: Face-to-Face Contacts Dataset Degree protector: k = 2

  31. Case: Face-to-Face Contacts Dataset Degree protector: k = 2 Scheme1 Scheme2

  32. User Reviews • A live, hands-on demo about 30 minutes ✓ All protectors are easy to use ✓ Helps interpretation. ✓ A “fine - grained data processing” pipeline. ? Trouble with the provenance view.

  33. Discussion • Detailed guidance Prioritize these individuals Try not to modify these individuals Do not handle these individuals Terminals Directions (privacy preserving goals) (processing priorities)

  34. Discussion • Detailed guidance • Performance Pre-computation for metric values Lazy searches

  35. Discussion • Detailed guidance • Performance … • Extensibility Degree Subgraph Hub Fingerprint Protector Protector Protector

  36. Thank you Xumeng Wang, Wei Chen, Jia-Kai Chou, Chris Bryan, Huihua Guan, Wenlong Chen, Rusheng Pan, Kwan-Liu Ma Acknowledgement National 973 Program of China (2015CB352503) National Natural Science Foundation of China ((61772456 and 61761136020) Alibaba-Zhejiang University Joint Institute of Frontier Technologies U.S. National Science Foundation (IIS-1320229 and IIS-1741536)

  37. Q&A GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms Xumeng Wang wangxumeng@zju.edu.cn Jia-Kai Chou jkchou@ucdavis.edu http://vidi.cs.ucdavis.edu/People/ChouJia-Kai Chris Bryan cbryan16@asu.edu

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