3 papers
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…
cs.CR2025
Making Sense of Private Advertising: A Principled Approach to a Complex Ecosystem
Kyle Hogan, Alishah Chator, Gabriel Kaptchuk +2
In this work, we model the end-to-end pipeline of the advertising ecosystem, allowing us to identify two main issues with the current trajectory of private advertising proposals. F…