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cs.LG2026
Private Prediction via PAC Privacy
Xiaochen Zhu, Mayuri Sridhar, Srinivas Devadas
Machine learning models are increasingly served behind APIs. This renders private prediction, i.e., privatizing a model's outputs rather than its parameters, a natural privacy targ…
cs.LG2026
PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization
Murat Bilgehan Ertan, Xiaochen Zhu, Phuong Ha Nguyen +2
We introduce PACZero, a family of PAC-private zeroth-order mechanisms for fine-tuning large language models that delivers usable utility at . This privacy regime…