Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage
Fuente:
arXiv
Saved in:
| Main Authors: | Meng, Jiayang, Huang, Tao, Chen, Hong, Shi, Xin, Huang, Qingyu, Hou, Chen |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Enhanced Privacy Leakage from Noise-Perturbed Gradients via Gradient-Guided Conditional Diffusion Models
by: Meng, Jiayang, et al.
Published: (2025)
by: Meng, Jiayang, et al.
Published: (2025)
Observable Channels, Not Just Storage: Evaluating Privacy Leakage in LLM Agent Pipelines
by: Huang, Tao, et al.
Published: (2026)
by: Huang, Tao, et al.
Published: (2026)
Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
by: Huang, Tao, et al.
Published: (2024)
by: Huang, Tao, et al.
Published: (2024)
Intermediate Outputs Are More Sensitive Than You Think
by: Huang, Tao, et al.
Published: (2024)
by: Huang, Tao, et al.
Published: (2024)
Metric-Normalized Posterior Leakage (mPL): Attacker-Aligned Privacy for Joint Consumption
by: Chen, Gaoyi, et al.
Published: (2026)
by: Chen, Gaoyi, et al.
Published: (2026)
Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering
by: Rashid, Md Rafi Ur, et al.
Published: (2023)
by: Rashid, Md Rafi Ur, et al.
Published: (2023)
Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation
by: Shi, Haonan, et al.
Published: (2025)
by: Shi, Haonan, et al.
Published: (2025)
Analysis of Privacy Leakage in Federated Large Language Models
by: Vu, Minh N., et al.
Published: (2024)
by: Vu, Minh N., et al.
Published: (2024)
Defeating Cerberus: Concept-Guided Privacy-Leakage Mitigation in Multimodal Language Models
by: Zhang, Boyang, et al.
Published: (2025)
by: Zhang, Boyang, et al.
Published: (2025)
Is Diffusion Model Safe? Severe Data Leakage via Gradient-Guided Diffusion Model
by: Meng, Jiayang, et al.
Published: (2024)
by: Meng, Jiayang, et al.
Published: (2024)
Evaluating Privacy Leakage in Split Learning
by: Qiu, Xinchi, et al.
Published: (2023)
by: Qiu, Xinchi, et al.
Published: (2023)
Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack
by: Lumbut, Chayadon, et al.
Published: (2024)
by: Lumbut, Chayadon, et al.
Published: (2024)
Calibrating Practical Privacy Risks for Differentially Private Machine Learning
by: Gu, Yuechun, et al.
Published: (2024)
by: Gu, Yuechun, et al.
Published: (2024)
A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage
by: Xin, Rui, et al.
Published: (2025)
by: Xin, Rui, et al.
Published: (2025)
Sanitize Your Responses: Mitigating Privacy Leakage in Large Language Models
by: Fu, Wenjie, et al.
Published: (2025)
by: Fu, Wenjie, et al.
Published: (2025)
Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
by: Ward, Joshua, et al.
Published: (2025)
by: Ward, Joshua, et al.
Published: (2025)
DeepLeak: Privacy Enhancing Hardening of Model Explanations Against Membership Leakage
by: Hmida, Firas Ben, et al.
Published: (2026)
by: Hmida, Firas Ben, et al.
Published: (2026)
Privacy Leakage via Output Label Space and Differentially Private Continual Learning
by: Tobaben, Marlon, et al.
Published: (2024)
by: Tobaben, Marlon, et al.
Published: (2024)
On the Privacy Risk of In-context Learning
by: Duan, Haonan, et al.
Published: (2024)
by: Duan, Haonan, et al.
Published: (2024)
Panther: A Cost-Effective Privacy-Preserving Framework for GNN Training and Inference Services in Cloud Environments
by: Chen, Congcong, et al.
Published: (2025)
by: Chen, Congcong, et al.
Published: (2025)
BlocksecRT-DETR: Decentralized Privacy-Preserving and Token-Efficient Federated Transformer Learning for Secure Real-Time Object Detection in ITS
by: Tahera, Mohoshin Ara, et al.
Published: (2026)
by: Tahera, Mohoshin Ara, et al.
Published: (2026)
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
by: Zhao, Tianyu, et al.
Published: (2025)
by: Zhao, Tianyu, et al.
Published: (2025)
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
by: Li, Zhuohang, et al.
Published: (2024)
by: Li, Zhuohang, et al.
Published: (2024)
FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation
by: Gu, Yuechun, et al.
Published: (2024)
by: Gu, Yuechun, et al.
Published: (2024)
The DCR Delusion: Measuring the Privacy Risk of Synthetic Data
by: Yao, Zexi, et al.
Published: (2025)
by: Yao, Zexi, et al.
Published: (2025)
Agentic Privacy-Preserving Machine Learning
by: Zhang, Mengyu, et al.
Published: (2025)
by: Zhang, Mengyu, et al.
Published: (2025)
Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning
by: Kim, Joon, et al.
Published: (2024)
by: Kim, Joon, et al.
Published: (2024)
Understanding Deep Gradient Leakage via Inversion Influence Functions
by: Zhang, Haobo, et al.
Published: (2023)
by: Zhang, Haobo, et al.
Published: (2023)
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
by: Shao, Jiawei, et al.
Published: (2023)
by: Shao, Jiawei, et al.
Published: (2023)
Driving Privacy Forward: Mitigating Information Leakage within Smart Vehicles through Synthetic Data Generation
by: Parikh, Krish
Published: (2024)
by: Parikh, Krish
Published: (2024)
Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk
by: Li, Zhangheng, et al.
Published: (2024)
by: Li, Zhangheng, et al.
Published: (2024)
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory
by: Zhang, Xiaojin, et al.
Published: (2024)
by: Zhang, Xiaojin, et al.
Published: (2024)
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective
by: Zeng, Zhihao, et al.
Published: (2025)
by: Zeng, Zhihao, et al.
Published: (2025)
What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference
by: Fan, Mingyuan, et al.
Published: (2026)
by: Fan, Mingyuan, et al.
Published: (2026)
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
by: Fan, Mingyuan, et al.
Published: (2022)
by: Fan, Mingyuan, et al.
Published: (2022)
Privacy-aware Gaussian Process Regression
by: Tuo, Rui, et al.
Published: (2023)
by: Tuo, Rui, et al.
Published: (2023)
Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference
by: Huang, Catherine, et al.
Published: (2024)
by: Huang, Catherine, et al.
Published: (2024)
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients
by: Cundy, Chris, et al.
Published: (2020)
by: Cundy, Chris, et al.
Published: (2020)
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
by: Greenewald, Kristjan, et al.
Published: (2024)
by: Greenewald, Kristjan, et al.
Published: (2024)
Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant
by: Chiang, John
Published: (2022)
by: Chiang, John
Published: (2022)
Similar Items
-
Enhanced Privacy Leakage from Noise-Perturbed Gradients via Gradient-Guided Conditional Diffusion Models
by: Meng, Jiayang, et al.
Published: (2025) -
Observable Channels, Not Just Storage: Evaluating Privacy Leakage in LLM Agent Pipelines
by: Huang, Tao, et al.
Published: (2026) -
Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
by: Huang, Tao, et al.
Published: (2024) -
Intermediate Outputs Are More Sensitive Than You Think
by: Huang, Tao, et al.
Published: (2024) -
Metric-Normalized Posterior Leakage (mPL): Attacker-Aligned Privacy for Joint Consumption
by: Chen, Gaoyi, et al.
Published: (2026)