ZK-APEX is a zero-shot verifiable approximate unlearning framework that applies sparse masking and blockwise empirical Fisher compensation on personalized models and uses Halo2 proofs to confirm correct execution without revealing client data.
Title resolution pending
2 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
2
Pith papers citing it
1
external citations · external index
years
2025 2verdicts
UNVERDICTED 2representative citing papers
ZKMLOps is an MLOps framework that uses zero-knowledge proofs to generate verifiable cryptographic evidence of AI model compliance without revealing confidential information.
citing papers explorer
-
ZK-APEX: Zero-Knowledge Approximate Personalized Unlearning with Executable Proofs
ZK-APEX is a zero-shot verifiable approximate unlearning framework that applies sparse masking and blockwise empirical Fisher compensation on personalized models and uses Halo2 proofs to confirm correct execution without revealing client data.
-
"Show Me You Comply... Without Showing Me Anything": Zero-Knowledge Software Auditing for AI-Enabled Systems
ZKMLOps is an MLOps framework that uses zero-knowledge proofs to generate verifiable cryptographic evidence of AI model compliance without revealing confidential information.