Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise

• URL: http://arxiv.org/abs/2508.15523v1
• Date: Thu, 21 Aug 2025 12:54:19 GMT
• Title: Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise
• Abstract summary: SPOF is a differentially private training mechanism intended for multi-user environments.<n>It exhibits to environmental noise during training, maintaining stable performance even when user inputs are corrupted.<n>Our results show that SPOF achieves up to 3.5% higher reconstruction accuracy and reduces mean training time by up to 57.2% compared to DP-SGD.
• Score: 7.010398112169834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose SPOF (Stabilization of Perturbed Loss Function), a differentially private training mechanism intended for multi-user local differential privacy (LDP). SPOF perturbs a stabilized Taylor expanded polynomial approximation of a model's training loss function, where each user's data is privatized by calibrated noise added to the coefficients of the polynomial. Unlike gradient-based mechanisms such as differentially private stochastic gradient descent (DP-SGD), SPOF does not require injecting noise into the gradients of the loss function, which improves both computational efficiency and stability. This formulation naturally supports simultaneous privacy guarantees across all users. Moreover, SPOF exhibits robustness to environmental noise during training, maintaining stable performance even when user inputs are corrupted. We compare SPOF with a multi-user extension of DP-SGD, evaluating both methods in a wireless body area network (WBAN) scenario involving heterogeneous user data and stochastic channel noise from body sensors. Our results show that SPOF achieves, on average, up to 3.5% higher reconstruction accuracy and reduces mean training time by up to 57.2% compared to DP-SGD, demonstrating superior privacy-utility trade-offs in multi-user environments.

Related papers

• An Adaptive Gradient Clipping and Noise Injection Mechanism for Differentially Private Federated Learning [1.5876034680722233]
  This paper presents DDP-SA-adaptive, an adaptive gradient clipping and noise adding mechanism for differentially private federated learning.<n>Compared with the static DDP-SA baseline, DDP-SA-adaptive reduces the number of communication rounds by 6.81%, total training time by 19.21%, and average per-round training time by 13.33%.<n>It also reduces test loss by 98.74% and increases test R2 by 3.41%, leading to improved model accuracy.
  arXiv  Detail & Related papers  (2026-08-15T10:10:34Z)
• PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization [16.48480873870065]
  We introduce PLRV-O, a framework that defines a broad search space of parameterized DP-SGD noise distributions.<n>We show that PLRV-O substantially improves utility under strict privacy constraints.
  arXiv  Detail & Related papers  (2025-09-08T01:06:45Z)
• Machine Learning with Privacy for Protected Attributes [56.44253915927481]
  We refine the definition of differential privacy (DP) to create a more general and flexible framework that we call feature differential privacy (FDP)<n>Our definition is simulation-based and allows for both addition/removal and replacement variants of privacy, and can handle arbitrary separation of protected and non-protected features.<n>We apply our framework to various machine learning tasks and show that it can significantly improve the utility of DP-trained models when public features are available.
  arXiv  Detail & Related papers  (2025-06-24T17:53:28Z)
• Revisiting Privacy-Utility Trade-off for DP Training with Pre-existing Knowledge [40.44144653519249]
  We propose a generic differential privacy framework with heterogeneous noise (DP-Hero)<n>Atop DP-Hero, we instantiate a heterogeneous version of DP-SGD, and further extend it to federated training.<n>We conduct comprehensive experiments to verify and explain the effectiveness of the proposed DP-Hero, showing improved training accuracy compared with state-of-the-art works.
  arXiv  Detail & Related papers  (2024-09-05T08:40:54Z)
• Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation [91.83820250747935]
  Pseudo-label noise is mainly contained in unstable samples in which predictions of most pixels undergo significant variations during self-training. We introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples. SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings.
  arXiv  Detail & Related papers  (2024-06-10T21:44:52Z)
• Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy [67.33715954653098]
  We propose a novel DPFL algorithm named DP-FedSAM, which leverages gradient perturbation to mitigate the negative impact of DP. Specifically, DP-FedSAM integrates Sharpness Aware of Minimization (SAM) to generate local flatness models with stability and weight robustness. To further reduce the magnitude random noise while achieving better performance, we propose DP-FedSAM-$top_k$ by adopting the local update sparsification technique.
  arXiv  Detail & Related papers  (2023-05-01T15:19:09Z)
• Make Landscape Flatter in Differentially Private Federated Learning [69.78485792860333]
  We propose a novel DPFL algorithm named DP-FedSAM, which leverages gradient perturbation to mitigate the negative impact of DP. Specifically, DP-FedSAM integrates local flatness models with better stability and weight robustness, which results in the small norm of local updates and robustness to DP noise. Our algorithm achieves state-of-the-art (SOTA) performance compared with existing SOTA baselines in DPFL.
  arXiv  Detail & Related papers  (2023-03-20T16:27:36Z)
• Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning [86.08285033925597]
  This paper presents a new DP perturbation mechanism with a time-varying noise amplitude to protect the privacy of federated learning. We derive an online refinement of the series to prevent FL from premature convergence resulting from excessive perturbation noise. The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
  arXiv  Detail & Related papers  (2023-03-07T22:52:40Z)
• FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation [72.19198763459448]
  We learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise. These perturbed data densities are linked together by the Fokker-Planck equation (FPE), a partial differential equation (PDE) governing the spatial-temporal evolution of a density. We derive a corresponding equation called the score FPE that characterizes the noise-conditional scores of the perturbed data densities.
  arXiv  Detail & Related papers  (2022-10-09T16:27:25Z)
• A Differentially Private Framework for Deep Learning with Convexified Loss Functions [4.059849656394191]
  Differential privacy (DP) has been applied in deep learning for preserving privacy of the underlying training sets. Existing DP practice falls into three categories - objective perturbation, gradient perturbation and output perturbation. We propose a novel output perturbation framework by injecting DP noise into a randomly sampled neuron.
  arXiv  Detail & Related papers  (2022-04-03T11:10:05Z)
• Dynamic Differential-Privacy Preserving SGD [19.273542515320372]
  Differentially-Private Gradient Descent (DP-SGD) prevents training-data privacy breaches by adding noise to the clipped gradient during SGD training. The same clipping operation and additive noise across training steps results in unstable updates and even a ramp-up period. We propose the dynamic DP-SGD, which has a lower privacy cost than the DP-SGD during updates until they achieve the same target privacy budget.
  arXiv  Detail & Related papers  (2021-10-30T04:45:11Z)
• Differentially Private Federated Learning with Laplacian Smoothing [72.85272874099644]
  Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. An adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models.
  arXiv  Detail & Related papers  (2020-05-01T04:28:38Z)

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.