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Multiparty Computation and Application

EPIC is a groundbreaking solution that enables efficient and private image classification through secure multiparty computation. It ensures active security, surpassing other protocols in related work, and achieves impressive performance gains over state-of-the-art systems. EPIC provides faster runtime, significant improvement in communication cost, and higher classification accuracy. Explore how EPIC can transform image classification with enhanced security, efficiency, and accuracy.

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Multiparty Computation and Application

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  1. Multiparty Computation and Application CRYP-F01 EleftheriaMakri Lecturer/Researcher Imec-COSIC, KU Leuven, BE & Saxion University of Applied Sciences, NL @MakriEleftheria

  2. EPIC: Efficient Private Image Classification (or: Learning from the Masters) E. Makri, D. Rotaru, N. P. Smart, F. Vercauteren

  3. EPIC: Efficient Private Image Classification

  4. Feature Extraction

  5. Transfer Learning Feature Extraction (or: Learning from the Masters)

  6. EPIC Security Active Security vs. Passive Security All other protocols in the related work EPIC

  7. Step 1: Create the ML model Alice Inception–v3 CNN Linear SVM Dragos Rotaru 9

  8. Step 2: Alice secret shares the ML model Charlie Alice Bob

  9. Step 2: Alice secret shares the ML model Charlie Alice Bob Dragos Rotaru + + =

  10. Step 3: Bob extracts features Bob Inception–v3 CNN Features Dragos Rotaru 12

  11. Step 4: Bob secret shares features Charlie Alice Bob Dragos Rotaru

  12. Step 4: Bob secret shares features Charlie Alice Bob Dragos Rotaru Dragos Rotaru + + = CNN-Feat( )

  13. Step 5: Parties use MPC to help Charlie compute label of SVM-Alice(Bob-Image) Charlie Alice Bob

  14. Step 5: Parties use MPC to help Charlie compute label of SVM-Alice(Bob-Image) Charlie Alice ***“Florist”*** Bob

  15. Video of the Demo of our work will appear here

  16. EPIC Performance – Simple Variant Computation Cost Communication Cost

  17. Performance of the state-of-the-art private image classification Computation Cost Communication Cost * JianLiu, MikaJuuti, YaoLu, N. Asokan. ObliviousNeural Network Predictionsvia MiniONNTransformations. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 619-631). ACM. ** ChiraagJuvekar, VinodVaikuntanathan, andAnanthaChandrakasan. GAZELLE: A low latencyframeworkfor secure neuralnetworkinference. In 27th USENIX Security Symposium (USENIX Security '18), Baltimore, MD, 2018. USENIX Association.

  18. EPIC Efficiency Gain over the state-of-the-art 1 ChiraagJuvekar, VinodVaikuntanathan, andAnanthaChandrakasan. GAZELLE: A low latencyframeworkfor secure neuralnetworkinference. In 27th USENIX Security Symposium (USENIX Security '18), Baltimore, MD, 2018. USENIX Association. • EPIC vs. Gazelle1 on CIFAR-10: • 34 times faster runtime; • 50 times improvement of communication cost; • 7% higher classification accuracy. • EPIC vs. Gazelle1 with the same accuracy: • 700 times faster runtime; • 500 times improvement of communication cost.

  19. Now what? • What would transform EPIC to a LEGENDARY solution? • Maintain security • Maintain or increase efficiency • Increase accuracy! • Any ideas on how to do this (using MPC)? • Talk to me during the break, or • Contact me offline at: eleftheria.makri@esat.kuleuven.be

  20. THAT WAS EPIC!

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