arXiv:2607.07209v1 Announce Type: cross Abstract: 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 backl
As federated and streaming learning systems become more prevalent, the need to ensure data privacy throughout the entire model development trajectory, especially against adaptive attacks, is critical.
This research provides a framework for auditable privacy assurances in continual learning, which is essential for deploying AI systems in sensitive applications and maintaining public trust.
The development of an auditable buffering-aggregation recipe offers a more robust method for protecting individual participation privacy in dynamic AI environments, moving beyond standard Hamming-neighbor analyses.
- · AI developers
- · Organizations handling sensitive data
- · Privacy-focused AI platforms
- · Adversaries exploiting data leakage
- · Systems with weak privacy guarantees
Increased adoption of federated and streaming learning in industries with strict privacy regulations.
New standards and certifications for privacy-preserving AI models emerge, driving market differentiation.
Public confidence in AI applications, particularly those handling personal data, grows significantly.
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Read at arXiv cs.LG