Privacy Amplification for the Gaussian Mechanism via Bounded Support
<|reference_start|>Privacy Amplification for the Gaussian Mechanism via Bounded Support: Data-dependent privacy accounting frameworks such as per-instance differential privacy (pDP) and Fisher information loss (FIL) confer fine-grained privacy guarantees for individuals in a fixed training dataset. These guarantees can be desirable compared to vanilla DP in real world settings as they tightly upper-bound the privacy leakage for a $\textit{specific}$ individual in an $\textit{actual}$ dataset, rather than considering worst-case datasets. While these frameworks are beginning to gain popularity, to date, there is a lack of private mechanisms that can fully leverage advantages of data-dependent accounting. To bridge this gap, we propose simple modifications of the Gaussian mechanism with bounded support, showing that they amplify privacy guarantees under data-dependent accounting. Experiments on model training with DP-SGD show that using bounded support Gaussian mechanisms can provide a reduction of the pDP bound $\epsilon$ by as much as 30% without negative effects on model utility.<|reference_end|>
@article{hu2024privacy,
    title={Privacy Amplification for the Gaussian Mechanism via Bounded Support},
    author={Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri,
  Chuan Guo},
    journal={arXiv preprint arXiv:2403.05598},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05598},
    primaryClass={cs.CR cs.LG}
}