Private Generative Bootstrap via Blocking
With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers. Equally important is to privatize the reporting of uncertainty in such answers. To this end, we adopt a Bayesian likelihood-free framework and make simulation from the posterior private. In particular, we propose a new private instantiation of the Bayesian bootstrap using a blocking strategy. Rather than assigning idiosyncratic random weights to each individual, we randomly group individuals and assign a single weight to each group. By concealing individuals' contributions within a group, we fortify differential privacy gates. We harness amortized inferen...
| Authors | Jinwon Sohn, Veronika Ročková |
|---|---|
| Primary category | stat.ML |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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