🎉 Crypto’Graph Paper Accepted at CODASPY 2024: Privacy-Preserving Link Prediction for Social Networks

We’re excited to announce that our research paper “Crypto’Graph” has been accepted and presented at CODASPY 2024! This work addresses a critical challenge in social media and graph analytics: enabling collaborative friend recommendations while preserving user privacy.

The Challenge: Collaborative Recommendations vs. Privacy

We’re all familiar with social media platforms suggesting new friends or connections based on the number of mutual friends we share. However, what if these platforms could collaborate on friend recommendations? This could lead to more accurate and relevant suggestions, but it also raises significant privacy concerns.

Our Solution: Crypto’Graph

Our research leverages cryptographic solutions to enable data owners structured as graphs (like social networks) to collaborate on link prediction while maintaining complete data confidentiality.

Key Research Contributions

🚀 Enhanced Computational Efficiency: We significantly improve the computational efficiency and generality of existing solutions for privacy-preserving link prediction on distributed graphs.

🛡️ False Link Identification: Our solution can also be used for securely and collaboratively identifying false links in graphs, enhancing the quality and reliability of social network data.

Technical Innovation

Crypto’Graph represents a breakthrough in privacy-preserving graph analytics:

  • Secure Multiparty Computation: Enables collaboration without data sharing
  • Distributed Graph Processing: Works across multiple organizations
  • Privacy Guarantees: Mathematical proofs of data confidentiality
  • Practical Implementation: Production-ready cryptographic protocols

Real-World Applications

Our findings have the potential to revolutionize:

Social Media Recommendations

  • Collaborative Filtering: Multiple platforms working together for better suggestions
  • Privacy-Preserving: User data never leaves the original platform
  • Improved Accuracy: Better recommendations through collaborative insights
  • User Trust: Maintaining privacy while enhancing functionality

Graph Data Processing

  • Secure Collaboration: Organizations can work together on graph analysis
  • Data Confidentiality: Sensitive graph structures remain protected
  • Regulatory Compliance: Meets privacy requirements like GDPR
  • Industry Applications: Healthcare, finance, cybersecurity, and more

Conference Presentation at CODASPY 2024

Presenting our work at CODASPY 2024 (ACM Conference on Data and Application Security and Privacy) was an excellent opportunity to:

  • Share Our Research: Present our findings to the cybersecurity community
  • Receive Feedback: Get valuable input from peers and experts
  • Network: Connect with researchers and practitioners in the field
  • Validate Impact: Demonstrate the practical relevance of our work

Research Team & Collaboration

This research was made possible through collaboration with:

  • Sofiane Azogagh: Research partner and co-author
  • SĂ©bastien Gambs: Academic supervisor and co-author
  • Marc-Olivier Killijian: Academic supervisor and co-author

We’re grateful for the support and collaboration that made this research possible.

Future Impact & Applications

We’re excited about the potential of our findings to:

  • Improve Social Network Recommendations: Better friend suggestions while preserving privacy
  • Enable Secure Graph Processing: Collaborative analysis without data sharing
  • Advance Privacy-Preserving Technologies: New tools for secure computation
  • Industry Adoption: Real-world deployment in social media and beyond

Research Significance

This work represents a significant step forward in:

  • Privacy-Preserving Machine Learning: Secure ML on distributed graph data
  • Social Network Analysis: Collaborative insights without privacy compromise
  • Cryptographic Protocols: Practical implementations for real-world use
  • Data Collaboration: Enabling cooperation while maintaining confidentiality

Our Crypto’Graph research demonstrates that privacy and collaboration are not mutually exclusive in social media and graph analytics. By leveraging advanced cryptographic techniques, we can enable better recommendations and insights while maintaining the highest standards of user privacy and data protection.

We’re excited to continue advancing the state-of-the-art in privacy-preserving technologies and to see our research applied in real-world social media platforms and beyond! 🚀🔒