Copertina

Spurious Privacy Leakage in Neural Networks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Chenxiang, Pang, Jun, Mauw, Sjouke
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
di: Zhang, Chenxiang, et al.
Pubblicazione: (2025)

How does the optimizer implicitly bias the model merging loss landscape?
di: Zhang, Chenxiang, et al.
Pubblicazione: (2025)

Neural Networks with Complex-Valued Weights Have No Spurious Local Minima
di: Liu, Xingtu
Pubblicazione: (2021)

Algorithm-Informed Graph Neural Networks for Leakage Detection and Localization in Water Distribution Networks
di: Zhang, Zepeng, et al.
Pubblicazione: (2024)

SCL-GNN: Towards Generalizable Graph Neural Networks via Spurious Correlation Learning
di: Zhang, Yuxiang, et al.
Pubblicazione: (2026)

Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation
di: Holstege, Floris, et al.
Pubblicazione: (2023)

Removing Spurious Correlation from Neural Network Interpretations
di: Fotouhi, Milad, et al.
Pubblicazione: (2024)

A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage
di: Xin, Rui, et al.
Pubblicazione: (2025)

Game-Theoretic Machine Unlearning: Mitigating Extra Privacy Leakage
di: Liu, Hengzhu, et al.
Pubblicazione: (2024)

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage
di: Meng, Jiayang, et al.
Pubblicazione: (2025)

Estimating Privacy Leakage of Augmented Contextual Knowledge in Language Models
di: Flemings, James, et al.
Pubblicazione: (2024)

Evaluating Privacy Leakage in Split Learning
di: Qiu, Xinchi, et al.
Pubblicazione: (2023)

Empirical Evaluation of Memory-Erasure Protocols
di: Gil-Pons, Reynaldo, et al.
Pubblicazione: (2025)

Unlinkability and History Preserving Bisimilarity
di: Aubert, Clément, et al.
Pubblicazione: (2026)

No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks
di: Refael, Yehonatan, et al.
Pubblicazione: (2025)

Analysis of Privacy Leakage in Federated Large Language Models
di: Vu, Minh N., et al.
Pubblicazione: (2024)

Defeating Cerberus: Concept-Guided Privacy-Leakage Mitigation in Multimodal Language Models
di: Zhang, Boyang, et al.
Pubblicazione: (2025)

Benchmarking Fairness in Spiking Neural Networks: Data Bias, Spurious Features, and Hardware Effects
di: He, Hudi, et al.
Pubblicazione: (2026)

Neural Optimal Transport in Hilbert Spaces: Characterizing Spurious Solutions and Gaussian Smoothing
di: Choi, Jae-Hwan, et al.
Pubblicazione: (2026)

A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
di: Shao, Jiawei, et al.
Pubblicazione: (2023)

Spurious Rewards: Rethinking Training Signals in RLVR
di: Shao, Rulin, et al.
Pubblicazione: (2025)

Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations
di: Le, Phuong Quynh, et al.
Pubblicazione: (2024)

MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage
di: Song, Ying, et al.
Pubblicazione: (2024)

Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation
di: Byun, Jessup, et al.
Pubblicazione: (2025)

Spurious Feature Diversification Improves Out-of-distribution Generalization
di: Lin, Yong, et al.
Pubblicazione: (2023)

Functional Encryption in Secure Neural Network Training: Data Leakage and Practical Mitigations
di: Ioniţă, Alexandru, et al.
Pubblicazione: (2025)

Elastic Representation: Mitigating Spurious Correlations for Group Robustness
di: Wen, Tao, et al.
Pubblicazione: (2025)

Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack
di: Lumbut, Chayadon, et al.
Pubblicazione: (2024)

Reassessing the Validity of Spurious Correlations Benchmarks
di: Bell, Samuel J., et al.
Pubblicazione: (2024)

Improving Group Robustness on Spurious Correlation via Evidential Alignment
di: Ye, Wenqian, et al.
Pubblicazione: (2025)

NeuronTune: Towards Self-Guided Spurious Bias Mitigation
di: Zheng, Guangtao, et al.
Pubblicazione: (2025)

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks
di: Guo, Zhishuai, et al.
Pubblicazione: (2026)

Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation
di: Shi, Haonan, et al.
Pubblicazione: (2025)

When Privacy Isn't Synthetic: Hidden Data Leakage in Generative AI Models
di: Mustaqim, S. M., et al.
Pubblicazione: (2025)

Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
di: Ward, Joshua, et al.
Pubblicazione: (2025)

DeepLeak: Privacy Enhancing Hardening of Model Explanations Against Membership Leakage
di: Hmida, Firas Ben, et al.
Pubblicazione: (2026)

Privacy Leakage via Output Label Space and Differentially Private Continual Learning
di: Tobaben, Marlon, et al.
Pubblicazione: (2024)

Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
di: Zhang, He, et al.
Pubblicazione: (2023)

The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations
di: LaBonte, Tyler, et al.
Pubblicazione: (2024)

Training on Plausible Counterfactuals Removes Spurious Correlations
di: Sadiku, Shpresim, et al.
Pubblicazione: (2025)