: Multivariate Polynomial Commitments with Two-Group-Element Openings
Wei-Jie Wang, Alin Tomescu, Rex Fernando et al.
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Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation
This paper presents Maverick, a novel approach to private and verifiable LLM inference based on a protocol for delegating matrix-vector multiplication, a dominant operation in LLMs, and combines this verification primitive with LPN-based pseudorandom masking to provide input privacy.
Ben Merbaum, M. Raeisi, Wenhao Wang et al.
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Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees
PANDA is a scalable system that uses zero-knowledge proofs to prove the robustness and fairness properties of a model without revealing its private parameters, and can generate proofs of local robustness for neural networks with more than 2.9M parameters in 5 minutes, and can verify them in 10 seconds.
Youwei Zhong, Ben Merbaum, Timos Antonopoulos et al.
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