GS-Chaff: Multi-Agent Prompt-Level Semantic Chaffing for Privacy-Preserving LLM Inference.
- DOI
- 10.3390/s26175385
- Published
- 2026 Aug 26
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.3390/s26175385,
title = {GS-Chaff: Multi-Agent Prompt-Level Semantic Chaffing for Privacy-Preserving LLM Inference.},
author = {Zhou Q and Wang Z and Yue Z and Cai L and Liu K and Qin C},
year = {2026},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s26175385},
url = {https://doi.org/10.3390/s26175385}
}RIS
TY - JOUR TI - GS-Chaff: Multi-Agent Prompt-Level Semantic Chaffing for Privacy-Preserving LLM Inference. AU - Zhou Q AU - Wang Z AU - Yue Z AU - Cai L AU - Liu K AU - Qin C PY - 2026 JO - Sensors (Basel, Switzerland) DO - 10.3390/s26175385 UR - https://doi.org/10.3390/s26175385 ER -
APA
Q, Z., Z, W., Z, Y., L, C., K, L., & C, Q. (2026). GS-Chaff: Multi-Agent Prompt-Level Semantic Chaffing for Privacy-Preserving LLM Inference.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26175385
Source records
- pubmed · retrieved 2026-09-25T06:54:52.022Z