Continual Learning With Participation Privacy: An Auditable Buffering-Aggregation Recipe
Modern federated and streaming learning systems often release intermediate models, so privacy must hold for the full trajectory under adaptive interaction. Motivated by participation privacy, we study single-edit neighboring user streams, where one insertion/deletion shifts all subsequent updates and defeats standard Hamming-neighbor continual-release analyses. We give an auditable modular recipe. A randomized buffering wrapper emits bins of size $[U,2U]$, reducing single-edit streams to a Hamming-style per-bin update stream with explicit backlog/delay guarantees, where $U$ is calibrated by the privacy parameters $(\varepsilon,δ)$. We then prove a certification theorem identifying when a ...
| Authors | T-H. Hubert Chan, Elaine Shi, Mengshi Zhao et al. |
|---|---|
| Primary category | cs.CR |
| Published | 8 July 2026 |
| Last updated at source | 25 August 2026 |
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