Continual Learning With Participation Privacy: An Auditable Buffering-Aggregation Recipe

• URL: http://arxiv.org/abs/2607.07209v1
• Date: Wed, 08 Jul 2026 09:44:27 GMT
• Title: Continual Learning With Participation Privacy: An Auditable Buffering-Aggregation Recipe
• Abstract summary: We study single-edit neighboring user streams, where one insertion/deletion shifts all subsequent updates.<n>A randomized buffering wrapper emits bins of size $[U,2U]$, reducing single-edit streams to a Hamming-style per-bin update stream.<n>We prove a certification theorem identifying when a non-adaptive Hamming-neighbor DP proof for a continual primitive lifts to adaptive inputs.
• Score: 22.282174850598953
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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 $U$.

Related papers

• Shuffling is Not Enough: Breaking Permutation-Based Model Confidentiality in Hybrid FHE Inference [5.67775962599719]
  Hybrid fully homomorphic encryption(FHE) inference improves the practicality of private inference.<n>Recent schemes attempt to protect model confidentiality by returning noisy, output-permuted responses.<n>We show that this protection fails in the correctness regime required by hybrid FHE systems.
  arXiv  Detail & Related papers  (2026-09-11T14:37:41Z)
• Edit-Neighboring Data Streams and Privacy under Continual Observation [12.49920379845039]
  Differential privacy under Continual Observation (CO) quantifies the loss in privacy that occurs when outputs generated using a stream of sensitive input data are published in the online setting.<n>We consider a more stringent notion of privacy compared to prior work wherein an individual's participation may shift the entire stream by a time-step.<n>We show that we can recover the same additive error as in the standard notion of privacy albeit with worse constant coefficients for both arbitrary input streams and sparse streams.
  arXiv  Detail & Related papers  (2026-07-22T20:54:44Z)
• Locally Private Online Quantile Regression: Estimation and Inference [6.828516229529806]
  We study estimation and inference for online quantile regression under a one-report user-level $eps$-locally differentially private protocol.<n>For fixed finite channel designs, we establish local privacy, decoder unbiasedness, consistency, inference normality, and Hessian-free self-normalized scalars.
  arXiv  Detail & Related papers  (2026-07-06T16:52:54Z)
• Let's Ask Gauss: Improved One-Run Privacy Auditing [6.481993597228151]
  We study empirical privacy auditing for differentially private (DP) machine learning.<n>We develop a DP-auditing framework that leads to tighter privacy lower bounds from a single training run.
  arXiv  Detail & Related papers  (2026-06-10T22:44:47Z)
• Perfectly-Private Analog Secure Aggregation in Federated Learning [51.61616734974475]
  In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model.<n>In this paper, a novel secure parameter aggregation method is proposed that employs the torus rather than a finite field.
  arXiv  Detail & Related papers  (2025-09-10T15:22:40Z)
• Smoothed Normalization for Efficient Distributed Private Optimization [54.197255548244705]
  Federated learning enables machine learning models with privacy of participants.<n>There is no differentially private distributed method for training, non-feedback problems.<n>We introduce a new distributed algorithm $alpha$-$sf NormEC$ with provable convergence guarantees.
  arXiv  Detail & Related papers  (2025-02-19T07:10:32Z)
• Scalable DP-SGD: Shuffling vs. Poisson Subsampling [61.19794019914523]
  We provide new lower bounds on the privacy guarantee of the multi-epoch Adaptive Linear Queries (ABLQ) mechanism with shuffled batch sampling. We show substantial gaps when compared to Poisson subsampling; prior analysis was limited to a single epoch. We introduce a practical approach to implement Poisson subsampling at scale using massively parallel computation.
  arXiv  Detail & Related papers  (2024-11-06T19:06:16Z)
• Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling [2.8544822698499255]
  We propose a posterior sampling scheme from a generalised posterior targeting the minimisation of the $beta$-divergence between the model and the data generating process. This provides private estimation that is generally applicable without requiring changes to the underlying model. We show that $beta$D-Bayes produces more precise inference estimation for the same privacy guarantees.
  arXiv  Detail & Related papers  (2023-07-11T12:00:15Z)
• Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization [94.06564567766475]
  DP-SGD and DP-NSGD mitigate the risk of large models memorizing sensitive training data. We show that these two algorithms achieve similar best accuracy while DP-NSGD is comparatively easier to tune than DP-SGD.
  arXiv  Detail & Related papers  (2022-06-27T03:45:02Z)
• Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling [49.43288037509783]
  We show that random shuffling amplifies differential privacy guarantees of locally randomized data. Our result is based on a new approach that is simpler than previous work and extends to approximate differential privacy with nearly the same guarantees.
  arXiv  Detail & Related papers  (2020-12-23T17:07:26Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.