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 non-adaptive Hamming-neighbor DP proof for a continual primitive lifts to adaptive inputs: the primitive must use fresh per-round randomness and have a stable one-round privacy profile under common adaptive context. Together, these ingredients yield trajectory-level (varepsilon,δ)-DP for single-edit streams using standard primitives (e.g., tree prefix sums), with an explicit privacy--latency link via U.