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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…
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.LG2023
Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation
Xiaochen Zhu, Vincent Y. F. Tan, Xiaokui Xiao
Graph neural networks (GNNs) have gained an increasing amount of popularity due to their superior capability in learning node embeddings for various graph inference tasks, but trai…