PPEDCRF: Dynamic-CRF-Guided Selective Perturbation for Background-Based Location Privacy in Video Sequences

• URL: http://arxiv.org/abs/2604.17163v1
• Date: Sat, 18 Apr 2026 22:52:51 GMT
• Title: PPEDCRF: Dynamic-CRF-Guided Selective Perturbation for Background-Based Location Privacy in Video Sequences
• Abstract summary: PPEDCRF protects emphbackground-based location privacy in released video frames against gallery-based retrieval attackers.<n>It estimates location-sensitive background regions with a conditional random field (DCRF), rescaling strength with a normalized control penalty (NCP), and injecting Gaussian noise only inside the inferred regions via a DP-style calibration rule.
• Score: 7.172756746172751
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose PPEDCRF, a calibrated selective perturbation framework that protects \emph{background-based location privacy} in released video frames against gallery-based retrieval attackers. Even after GPS metadata are stripped, an adversary can geolocate a frame by matching its background visual cues to geo-tagged reference imagery; PPEDCRF mitigates this threat by estimating location-sensitive background regions with a dynamic conditional random field (DCRF), rescaling perturbation strength with a normalized control penalty (NCP), and injecting Gaussian noise only inside the inferred regions via a DP-style calibration rule. On a controlled paired-scene retrieval benchmark with eight attacker backbones and three noise seeds, PPEDCRF reduces ResNet18 Top-1 retrieval accuracy from 0.667 to $0.361\pm0.127$ at $σ_0=8$ while preserving $36.14\,$dB PSNR -- an ${\approx}6\,$dB quality advantage over global Gaussian noise. Transfer across the eight-backbone seed-averaged benchmark is broadly supportive (23 of 24 backbone-gallery cells show negative $Δ$), while appendix-scale confirmation identifies MixVPR as a remaining adverse-transfer exception. Matched-operating-point analysis shows that PPEDCRF and global Gaussian noise converge in Top-1 privacy at equal utility, so the practical benefit is spatially concentrated perturbation that preserves higher visual quality at any given noise scale rather than stronger matched-utility privacy. Code: https://github.com/mabo1215/PPEDCRF

Related papers

• EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy [8.888244401762996]
  Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data.<n>But their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information.<n>We propose EdgeRefine, a local differential privacy framework that improves this trade-off through adaptive edge refinement.
  arXiv  Detail & Related papers  (2026-07-09T16:28:05Z)
• TSDCRF: Balancing Privacy and Multi-Object Tracking via Time-Series CRF and Normalized Control Penalty [7.172756746172751]
  Multi-object tracking in video often requires appearance or location cues that can reveal sensitive identity information.<n>We propose TSDCRF, a plug-in refinement framework that balances privacy and tracking by combining three components.<n>Results show that TSDCRF achieves a better privacy--utility trade-off than white noise and prior methods.
  arXiv  Detail & Related papers  (2026-03-14T00:27:12Z)
• PPEDCRF: Privacy-Preserving Enhanced Dynamic CRF for Location-Privacy Protection for Sequence Videos with Minimal Detection Degradation [7.329775099113131]
  This paper studies location-privacy leakage under a background-based retrieval attacker.<n>It proposes PPEDCRF, a privacy-preserving conditional random field framework.<n>Experiments on public driving datasets demonstrate that PPEDCRF significantly reduces location-retrieval attack success.
  arXiv  Detail & Related papers  (2026-03-02T08:18:53Z)
• Blind Ultrasound Image Enhancement via Self-Supervised Physics-Guided Degradation Modeling [4.619828919345113]
  Supervised enhancement methods assume access to clean targets or known degradations.<n>We present a blind, self-supervised enhancement framework that jointly deconvolves and denoises B-mode images.
  arXiv  Detail & Related papers  (2026-01-29T15:28:25Z)
• LoRA and Privacy: When Random Projections Help (and When They Don't) [55.65932772290123]
  We introduce the (Wishart) projection mechanism, a randomized map of the form $S mapsto M f(S)$ with $M sim W_d (1/r I_d, r)$ and study its differential privacy properties.<n>For vector-valued queries $f$, we prove non-asymptotic DP guarantees without any additive noise, showing that Wishart randomness alone can suffice.<n>For matrix-valued queries, however, we establish a sharp negative result: in the noise-free setting, the mechanism is not DP, and we demonstrate its vulnerability.
  arXiv  Detail & Related papers  (2026-01-29T13:43:37Z)
• DPSR: Differentially Private Sparse Reconstruction via Multi-Stage Denoising for Recommender Systems [5.237172334460829]
  Differential privacy has emerged as the gold standard for protecting user data in recommender systems.<n>Existing privacy-preserving mechanisms face a fundamental challenge as privacy budgets tighten.<n>We introduce DPSR, a novel three-stage denoising framework.
  arXiv  Detail & Related papers  (2025-12-22T00:43:29Z)
• Fast Fourier Transform-Based Spectral and Temporal Gradient Filtering for Differential Privacy [1.5766082819987293]
  We introduce the FFT-Enhanced Kalman Filter (FFTKF), a differentially private optimization method that improves gradient quality.<n>FFTKF applies frequency-domain filtering to shift privacy noise into less informative high-frequency components.<n>It achieves higher test accuracy than DP-SGD and DiSK on MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet with CNNs, Wide ResNets, and Vision Transformers.
  arXiv  Detail & Related papers  (2025-05-07T14:38:58Z)
• Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds [61.50022257278769]
  Noise is added to the activations in the last layers prior to the final classifiers or other task-specific layers. Lower bounding the variance of every possible unbiased estimator of the inputs quantifies the confidentiality arising from such added noise. Numerical experiments indicate that the HCR bounds are on the precipice of being effectual for small neural nets.
  arXiv  Detail & Related papers  (2024-04-03T16:58:03Z)
• Learning Global-aware Kernel for Image Harmonization [55.614444363743765]
  Image harmonization aims to solve the visual inconsistency problem in composited images by adaptively adjusting the foreground pixels with the background as references. Existing methods employ local color transformation or region matching between foreground and background, which neglects powerful proximity prior and independently distinguishes fore-/back-ground as a whole part for harmonization. We propose a novel Global-aware Kernel Network (GKNet) to harmonize local regions with comprehensive consideration of long-distance background references.
  arXiv  Detail & Related papers  (2023-05-19T13:49:02Z)
• Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising [73.71324390085714]
  We propose a novel perspective to solve the problem of real-world sRGB image denoising. We take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. We present a locally aware network (LAN) to meet the requirement, while LAN itself is supervised with the output of BNN.
  arXiv  Detail & Related papers  (2023-03-27T06:18:20Z)
• 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)
• RDP-GAN: A R\'enyi-Differential Privacy based Generative Adversarial Network [75.81653258081435]
  Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection. However, when GANs are applied on sensitive or private training examples, such as medical or financial records, it is still probable to divulge individuals' sensitive and private information. We propose a R'enyi-differentially private-GAN (RDP-GAN), which achieves differential privacy (DP) in a GAN by carefully adding random noises on the value of the loss function during training.
  arXiv  Detail & Related papers  (2020-07-04T09:51:02Z)

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.