The paper introduces SHIFT, a training-free attack that exploits the vulnerability of diffusion-based watermarking by stochastically deflecting the generative trajectory, achieving high removal rates…

Ghost introduces a manifold-aligned framework to generate plausible, unlearnable synthetic check-in trajectories that significantly degrade an attacker's ability to predict future locations.

Ghost introduces a manifold-aligned framework to generate plausible yet unlearnable synthetic check-in trajectories, significantly degrading the accuracy of next-POI prediction models without sacrific…

The paper introduces a novel, efficient mechanism based on permute-and-flip for applying differential privacy to symbolic state trajectories, significantly reducing the computational overhead compared…

The paper proposes HTP, a novel framework that leverages Large Language Models (LLMs) to first generate abstract travel patterns and then synthesize realistic GPS points, significantly improving traje…

The paper proposes Context-aware Metric Differential Privacy (C-mDP), a framework that improves vehicle location privacy by modeling temporal dependencies, achieving higher data utility than standard…

DiffCrossGait proposes a novel trajectory-level alignment method using latent diffusion to overcome domain discrepancies in 2D-3D gait recognition, achieving state-of-the-art performance.

PrivFedTalk introduces a privacy-aware federated framework for personalized talking-head generation by combining a shared diffusion backbone with local LoRA identity adapters and robust aggregation te…

The paper introduces a differentially private manifold denoising framework that allows noisy, non-private query points to be corrected using sensitive reference data while providing formal $(\varepsil…

The paper proposes MaskDiff-AD, a forward-only masked diffusion model trained on nominal data to achieve state-of-the-art anomaly detection across various categorical, mixed-type, and text datasets.

The paper introduces Diversity-inducing Initialization (DivIn), a novel method that improves image diversity by re-weighting the initial noise selection based on the guidance potential, thereby mitiga…

The paper proposes Trajectory Induced Preference Optimization (TIPO) to improve mobile GUI agent personalization by explicitly modeling and optimizing for privacy-related behavioral differences in exe…

The paper introduces MIRAGE, a framework that systematically discovers semantic attacks on online HD map construction by finding plausible environmental variations that bypass standard adversarial def…

The paper introduces PAS, a structured privacy mechanism that encodes user location using relative anchors, enabling location privacy in spatial RAG systems while maintaining high retrieval performanc…

UMEDA introduces a novel graph federated learning framework that uses spectral signal processing and diffusion models to enable privacy-preserving, robust localization across clients with highly heter…

The paper demonstrates that off-the-shelf image diffusion models, like Stable Diffusion, can be repurposed to generate synthetic structured data, posing a threat of ground truth drift in closed eviden…

The paper proposes TDPM, a time-aware diffusion model for generative recommendation, which significantly improves recommendation accuracy by explicitly modeling the non-stationary, time-evolving natur…

The paper proposes SubPopMark, a novel subpopulation-driven framework that injects harmless, verifiable markers into distilled datasets to prevent copyright infringement and data leakage.

The paper proposes a novel four-stage simulation framework that uses GPS-derived seasonal spatial priors and LLMs to generate demographically accurate, synthetic tourist mobility schedules for urban p…

The paper introduces CityTrajBench, a unified benchmark framework that standardizes the evaluation of city-scale vehicle trajectory generation, demonstrating that no single generation model dominates…