LinkMirage: Enabling Privacy-preserving Analytics on Social - PowerPoint PPT Presentation

Outline Introduction LinkMirage Conclusion LinkMirage: Enabling Privacy-preserving Analytics on Social Relationships Changchang Liu, Prateek Mittal Email: cl12@princeton.edu, pmittal@princeton.edu Princeton University February 23, 2016 1 / 18

Outline Introduction Social Relationships LinkMirage Privacy-utility tradeoff Conclusion Social relationships (a) (b) Third party applications rely on users’ social relationships: • E-commerce • Spam detection • Anonymous communication • Sybil defenses 2 / 18

Outline Introduction Social Relationships LinkMirage Privacy-utility tradeoff Conclusion Social relationships are very sensitive! Social relationships represent • Trusted friendships • Important interactions • Even more, business relations, etc. 3 / 18

Outline Introduction Social Relationships LinkMirage Privacy-utility tradeoff Conclusion How to balance utility and privacy? Privacy Utility Protect privacy of sensitive social relationships Preserve utility of obfuscated social relationships for real-world applications 4 / 18

Outline Introduction Social Relationships LinkMirage Privacy-utility tradeoff Conclusion Previous work of link privacy mechanisms To protect link privacy, previous work • obfuscate social relationships through link additions/deletions G G � p p add del However, previous work • only consider graph data where the links are redstatic 5 / 18

Outline Introduction Social Relationships LinkMirage Privacy-utility tradeoff Conclusion Limitations of previous link privacy mechanisms To protect link privacy, previous work • obfuscate social relationships through link additions/deletions G G � p p add del However, previous work • only consider graph data where the links are static 5 / 18

Outline Introduction Social Relationships LinkMirage Privacy-utility tradeoff Conclusion However, social networks are dynamic Temporal Facebook dataset (every three months) with 46,952 users and 876,993 edges 6 / 18

Outline Introduction Social Relationships LinkMirage Privacy-utility tradeoff Conclusion However, social networks are dynamic An adversary can combine the previously perturbed graphs together obfuscation G ¢ G 0 0 ... ... obfuscation G - ¢ G - t 1 t 1 obfuscation G G ¢ t t Adversary 7 / 18

Outline Introduction Social Relationships LinkMirage Privacy-utility tradeoff Conclusion Our Objective • Balance privacy and utility • Handle both the static and dynamic social network topologies • Provide rigorous privacy guarantees • Useful in real-world applications 8 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis LinkMirage LinkMirage Overview Algorithm Description Privacy Analysis Utility Analysis 8 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Social Relationship based Applications Q Original Graph Untrusted ( ) G Q G ¢ Applications Privacy-preserving graph analysis Sybil defenses OSN Anonymous communication providers ... 9 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Privacy-preserving Social Relationship based Applications Q Original Graph Untrusted ( ) G Q G ¢ Applications Privacy-preserving graph analysis Sybil defenses OSN Anonymous communication providers ... 9 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis LinkMirage Architecture LinkMirage System (Trusted) Obfuscation Algorithm Q Original Graph Obfuscated Graph Untrusted G ¢ ( ) G Q G ¢ Applications Privacy-preserving graph analysis Sybil defenses OSN LinkMirage Anonymous communication social link app providers User1 User2 User3 � ... 9 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Key intuitions • Naive method: independent perturbation − more information is leaked to others • We need to − incorporate graph evolution − leverage the information already released in previous graphs 10 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Algorithm Description G - t 1 11 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Algorithm Description G - t 1 1. clustering C 1 C 2 11 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Algorithm Description G - t 1 1. clustering C 1 C 2 2. perturbation ¢ G - C ¢ 1 t 1 C ¢ 2 11 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Algorithm Description 3. evolution G - G t 1 t 1. clustering C 1 C 2 2. perturbation ¢ G - C ¢ 1 t 1 C ¢ 2 11 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Algorithm Description 3. evolution G - G t 1 t 1. clustering 4. dynamic clustering C C 1 1 C 3 C C 2 2 2. perturbation ¢ G - C ¢ 1 t 1 C ¢ 2 11 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Algorithm Description 3. evolution G - G t 1 t 1. clustering 4. dynamic clustering C C 1 1 C 3 C C 2 2 2. perturbation 5. selective perturbation ¢ C ¢ C ¢ G ¢ G - C ¢ 3 1 1 t 1 t C ¢ C ¢ 2 2 11 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Algorithm Description 3. evolution G - G t 1 t 1. clustering Key step 1: dynamic clustering C C 1 1 C 3 C C 2 2 2. perturbation Key step 2. selective perturbation ¢ C ¢ C ¢ G ¢ G - C ¢ 3 1 1 t 1 t C ¢ C ¢ 2 2 11 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Two Key Steps in Our Algorithm Two key steps • Dynamic Clustering – find communities by simultaneously considering consecutive graphs – backtrack based on clustering result of the previous graph • Selective Perturbation – perturb the minimal amount of edges – use a very high privacy parameter while preserving structural properties (utility) 12 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Facebook Temporal Dataset (46,952 users and 876,993 edges) Original graphs t=3 t=4 t=5 Superior utility, due to dynamic clustering Utility advantage even exists in static scenario 13 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Utility Advantage Mittal et al. LinkMirage Original graphs t=3 t=4 t=5 Superior utility, due to dynamic clustering Utility advantage even exists in static scenario 13 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Privacy Advantage Original graphs Overlapped edges (black) and Changed edges (yellow) between consecutive graphs Superior privacy, due to selective perturbation 13 / 18

Outline LinkMirage Overview Introduction Algorithm Description LinkMirage Privacy Analysis Conclusion Utility Analysis Privacy Advantage Original graphs Mittal et al. LinkMirage Overlapped edges (black) and Changed edges (yellow) between consecutive graphs Superior privacy, due to selective perturbation 13 / 18

LinkMirage: Enabling Privacy-preserving Analytics on Social - PowerPoint PPT Presentation

Outline Introduction LinkMirage Conclusion LinkMirage: Enabling Privacy-preserving Analytics on Social Relationships Changchang Liu, Prateek Mittal Email: cl12@princeton.edu, pmittal@princeton.edu Princeton University February 23, 2016 1 /

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LinkMirage: Enabling Privacy-preserving Analytics on Social - PowerPoint PPT Presentation

Outline Introduction LinkMirage Conclusion LinkMirage: Enabling Privacy-preserving Analytics on Social Relationships Changchang Liu, Prateek Mittal Email: cl12@princeton.edu, pmittal@princeton.edu Princeton University February 23, 2016 1 /

PrivApprox Privacy-Preserving Stream Analytics https://privapprox.github.io Do Le

Sharemind - practical privacy- preserving analytics Sander Siim Cybernetica AS

Privacy Preserving Protocols Workshop on Cryptography for the Internet of Things Jens Hermans KU

Privacy-preserving Wi-Fi Analytics Barcelona, Spain PETS 2018 Mathieu Cunche Sbastien Gambs

Privacy-preserving Information Sharing: Crypto Tools and Applications Emiliano De Cristofaro

New Directions in Privacy- preserving Machine Learning Kamalika Chaudhuri University of

Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute Hung Dang, Anh

Preserving the Privacy of Sensitive Relationships in Graph Data Motivation Valuable Data! No

Collaborative Privacy Preserving Data Mining in Vertically Partitioned Databases Ehud Gudes

Privacy preserving data mining randomized response and association rule hiding Li Xiong

Privacy-preserving Biometrics in practice: diffjcult to sell Carmela Troncoso (Gradiant) My

Network Privacy Mostly issues of preserving privacy of data flowing through network Start

On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis James Foulds,* Joseph

How to get a trustworthy DNS Privacy enabling recursive resolver an analysis of authentication

Privacy-Preserving Query Processing over Encrypted Data in Cloud CS573 Data Privacy and Security

DIMACS/PORTIA Workshop on Privacy Preserving Data Mining Data Mining &amp; Information Privacy:

Identifying Causal Efgects in Experiments with Social Interactions and Non-compliance Francis J.

How do senators vote? -Issues - Strategies -Affiliation - Influence Voting Behavior 1

Social Interactions: A First-Person Perspective. A. Fathi, J. Hodgins, J. Rehg Presented by

Redefiniendo la Universidad Nuevos modelos informales para el aprendizaje en red Universitat de

Counting social interactions for discrete subsets of the plane Samantha Fairchild University of

Structural and geographic properties of online social interactions Yana Volkovich Barcelona

Human-Robot Social Interactions through Multimodal Deep Attention Recurrent Q-Network Nana Baah

CS525: Who s Your Best Friend? Targeted Privacy Attacks In Location sharing Social Networks

DIMACS/PORTIA Workshop on Privacy Preserving Data Mining Data Mining & Information Privacy: