Image de couverture de livre

Privacy Risks in Time Series Forecasting: User- and Record-Level Membership Inference

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Johansson, Nicolas, Olsson, Tobias, Nilsson, Daniel, Östman, Johan, Hoseini, Fazeleh
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Practical Feasibility of Gradient Inversion Attacks in Federated Learning
par: Valadi, Viktor, et autres
Publié: (2025)

Practical Bayes-Optimal Membership Inference Attacks
par: Lassila, Marcus, et autres
Publié: (2025)

Secure Aggregation is Not Private Against Membership Inference Attacks
par: Ngo, Khac-Hoang, et autres
Publié: (2024)

Adaptive Conformal Inference for Multi-Step Ahead Time-Series Forecasting Online
par: Szabadváry, Johan Hallberg
Publié: (2024)

Embedding-Space Data Augmentation to Prevent Membership Inference Attacks in Clinical Time Series Forecasting
par: Fracarolli, Marius, et autres
Publié: (2025)

Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
par: Liu, Zhenlong, et autres
Publié: (2024)

Membership Inference Attacks Against Time-Series Models
par: Koren, Noam, et autres
Publié: (2024)

On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis
par: Guan, Junyi, et autres
Publié: (2025)

VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks
par: Tsaprazlis, Efthymios, et autres
Publié: (2025)

Better Membership Inference Privacy Measurement through Discrepancy
par: Wu, Ruihan, et autres
Publié: (2024)

The Sample Complexity of Membership Inference and Privacy Auditing
par: Haghifam, Mahdi, et autres
Publié: (2025)

Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble
par: Wang, Zhiqi, et autres
Publié: (2025)

Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting
par: Wang, Jiacheng, et autres
Publié: (2026)

Membership Privacy Risks of Sharpness Aware Minimization
par: Kim, Young In, et autres
Publié: (2023)

Efficient Node Selection in Private Personalized Decentralized Learning
par: Zec, Edvin Listo, et autres
Publié: (2023)

Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy
par: Krüger, Fabian P., et autres
Publié: (2024)

Towards Holistic Disease Risk Prediction using Small Language Models
par: Björkdahl, Liv, et autres
Publié: (2024)

Membership Inference Attacks and Privacy in Topic Modeling
par: Manzonelli, Nico, et autres
Publié: (2024)

Approximate Probabilistic Inference for Time-Series Data A Robust Latent Gaussian Model With Temporal Awareness
par: Johansson, Anton, et autres
Publié: (2024)

MCMC for Bayesian estimation of Differential Privacy from Membership Inference Attacks
par: Yildirim, Ceren, et autres
Publié: (2025)

Membership Inference Attacks Expose Participation Privacy in ECG Foundation Encoders
par: Wang, Ziyu, et autres
Publié: (2026)

Explanations Leak: Membership Inference with Differential Privacy and Active Learning Defense
par: Ezzeddine, Fatima, et autres
Publié: (2026)

Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation
par: Mathisen, Johan Vik, et autres
Publié: (2024)

Free Record-Level Privacy Risk Evaluation Through Artifact-Based Methods
par: Pollock, Joseph, et autres
Publié: (2024)

Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
par: Zhang, Chenxiang, et autres
Publié: (2025)

Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning
par: Hu, Qiang, et autres
Publié: (2024)

A Critical Review on the Effectiveness and Privacy Threats of Membership Inference Attacks
par: Jebreel, Najeeb, et autres
Publié: (2026)

Decoupling Generalizability and Membership Privacy Risks in Neural Networks
par: Fang, Xingli, et autres
Publié: (2026)

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study
par: Aubinais, Eric, et autres
Publié: (2025)

Decoupled Subgraph Federated Learning
par: Aliakbari, Javad, et autres
Publié: (2024)

Timing is Important: Risk-aware Fund Allocation based on Time-Series Forecasting
par: Lyu, Fuyuan, et autres
Publié: (2025)

(Token-Level) InfoRMIA: Stronger Membership Inference and Memorization Assessment for LLMs
par: Tao, Jiashu, et autres
Publié: (2025)

Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference
par: Liu, Terrance, et autres
Publié: (2025)

Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
par: Wen, Yuxin, et autres
Publié: (2024)

Deconfounded Time Series Forecasting: A Causal Inference Approach
par: Gao, Wentao, et autres
Publié: (2024)

Learning with User-Level Local Differential Privacy
par: Zhao, Puning, et autres
Publié: (2024)

Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Models
par: Tian, Muhang, et autres
Publié: (2023)

Range Membership Inference Attacks
par: Tao, Jiashu, et autres
Publié: (2024)

Dual-Level Models for Physics-Informed Multi-Step Time Series Forecasting
par: Nasiri, Mahdi, et autres
Publié: (2026)

Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers
par: Atashgahi, Zahra, et autres
Publié: (2023)