most citedVerifiable Fine-Tuning for LLMs: Zero-Knowledge Training Proofs Bound to Data Provenance and Policy

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cs.CR2025

ZK-SenseLM: Verifiable Large-Model Wireless Sensing with Selective Abstention and Zero-Knowledge Attestation

Hasan Akgul, Mari Eplik, Javier Rojas +2

ZK-SenseLM is a secure and auditable wireless sensing framework that pairs a large-model encoder for Wi-Fi channel state information (and optionally mmWave radar or RFID) with a po…

cs.CR2025★ 1 cited

Verifiable Fine-Tuning for LLMs: Zero-Knowledge Training Proofs Bound to Data Provenance and Policy

Hasan Akgul, Daniel Borg, Arta Berisha +3

Large language models are often adapted through parameter efficient fine tuning, but current release practices provide weak assurances about what data were used and how updates wer…