🚀 TeleSparse: Practical Zero-Knowledge Proofs for Deep Neural Networks
Deep neural networks (DNNs) have transformed AI, powering breakthroughs in image recognition and language processing. But verifying these models without revealing sensitive details poses a significant privacy challenge. 🌐 TeleSparse makes zero-knowledge verification practical for today’s powerful neural networks!
🕵️♂️ Threat Model: Ensuring Privacy and Security
TeleSparse assumes a malicious verifier and prover scenario, focusing on protecting sensitive model weights and inputs while verifying correctness. The threat model ensures that while parties follow the protocol, they may try to infer private information indirectly. TeleSparse provides robust guarantees against such privacy leakages.
❓ Challenges Addressed by TeleSparse
- ⚙️ High Computational Overhead: Large models lead to extensive constraints, increasing computation and memory demands.
- 📚 Extensive Lookup Tables: Non-linear activations need massive lookup tables, further raising resource usage.
📊 System Overview
The figure below provides an overview of the TeleSparse system, showcasing its integration with the Halo2 proving system:
💡 Key Innovations of TeleSparse
🌳 Sparse-aware ZK Proof Generation
TeleSparse employs sparse-aware pruning, reducing unnecessary constraints by strategically removing weights. This approach maintains high accuracy while drastically cutting prover memory use by 67% and proof generation time by 46%.
🔄 Neural Network Teleportation – 📈 Activation Range Optimization
Neural network teleportation minimizes activation ranges, reducing lookup table sizes essential for zero-knowledge proofs. Teleportation optimizes activations, significantly streamlining the verification process. TeleSparse’s teleportation adjusts activation ranges to be narrower, reducing the lookup tables needed. The distribution below illustrates this for ResNet20:
🧪 Evaluation and Impressive Results
TeleSparse has been rigorously tested on popular architectures (Vision Transformers, ResNet, MobileNet) and datasets (CIFAR-10, CIFAR-100, ImageNet).
- 🚀 Faster Proof Generation: Remarkable reductions in prover memory usage and computation time.
- 🎯 Accuracy Retention: Only about a 1% accuracy drop—minimal compared to huge efficiency gains.
🛠 TeleSparse in Action
Using Halo2, known for efficient zero-knowledge proofs, TeleSparse leverages lightweight pruning and teleportation to deliver a scalable and efficient solution.
🌟 Why TeleSparse Matters
TeleSparse revolutionizes zero-knowledge proofs, enabling scalable, secure, and privacy-preserving AI. Dive deeper into TeleSparse on our GitHub repository or explore our arXiv paper! 🌐