Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Wu, Haoqi, Dai, Wei, Wang, Li, Yan, Qiang |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
ObCLIP: Oblivious CLoud-Device Hybrid Image Generation with Privacy Preservation
par: Wu, Haoqi, et autres
Publié: (2025)
par: Wu, Haoqi, et autres
Publié: (2025)
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
par: Redberg, Rachel, et autres
Publié: (2023)
par: Redberg, Rachel, et autres
Publié: (2023)
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
par: Wei, Yuecen, et autres
Publié: (2023)
par: Wei, Yuecen, et autres
Publié: (2023)
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence
par: Feng, Shuya, et autres
Publié: (2024)
par: Feng, Shuya, et autres
Publié: (2024)
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates
par: Wang, Chuanyin, et autres
Publié: (2025)
par: Wang, Chuanyin, et autres
Publié: (2025)
Convergent Differential Privacy Analysis for General Federated Learning
par: Sun, Yan, et autres
Publié: (2024)
par: Sun, Yan, et autres
Publié: (2024)
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
par: Tang, Xinyu, et autres
Publié: (2023)
par: Tang, Xinyu, et autres
Publié: (2023)
Revisiting Hyperparameter Tuning with Differential Privacy
par: Ding, Youlong, et autres
Publié: (2022)
par: Ding, Youlong, et autres
Publié: (2022)
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
par: Jiang, Yu, et autres
Publié: (2024)
par: Jiang, Yu, et autres
Publié: (2024)
SMA-DP: Spectral Memory-Aware Differential Privacy for Deep Learning
par: Partohaghighi, Mohammad, et autres
Publié: (2026)
par: Partohaghighi, Mohammad, et autres
Publié: (2026)
Differentially Private Relational Learning with Entity-level Privacy Guarantees
par: Huang, Yinan, et autres
Publié: (2025)
par: Huang, Yinan, et autres
Publié: (2025)
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy
par: Bao, Ergute, et autres
Publié: (2022)
par: Bao, Ergute, et autres
Publié: (2022)
Wasserstein Differential Privacy
par: Yang, Chengyi, et autres
Publié: (2024)
par: Yang, Chengyi, et autres
Publié: (2024)
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
par: You, Zhichao, et autres
Publié: (2025)
par: You, Zhichao, et autres
Publié: (2025)
DDP-SA: Scalable Privacy-Preserving Federated Learning via Distributed Differential Privacy and Secure Aggregation
par: Wei, Wenjing, et autres
Publié: (2026)
par: Wei, Wenjing, et autres
Publié: (2026)
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
par: Bhaila, Karuna, et autres
Publié: (2023)
par: Bhaila, Karuna, et autres
Publié: (2023)
Private Linear Regression with Differential Privacy and PAC Privacy
par: Yang, Hillary, et autres
Publié: (2024)
par: Yang, Hillary, et autres
Publié: (2024)
Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning
par: Nguyen, Quan Minh, et autres
Publié: (2026)
par: Nguyen, Quan Minh, et autres
Publié: (2026)
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
par: Li, Xiang, et autres
Publié: (2025)
par: Li, Xiang, et autres
Publié: (2025)
On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy
par: Wang, Zijian, et autres
Publié: (2025)
par: Wang, Zijian, et autres
Publié: (2025)
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
par: Gao, Fengyu, et autres
Publié: (2024)
par: Gao, Fengyu, et autres
Publié: (2024)
Purifying Approximate Differential Privacy with Randomized Post-processing
par: Lin, Yingyu, et autres
Publié: (2025)
par: Lin, Yingyu, et autres
Publié: (2025)
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
par: Wu, Jiapeng, et autres
Publié: (2023)
par: Wu, Jiapeng, et autres
Publié: (2023)
Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy
par: Chen, Wei-Ning, et autres
Publié: (2024)
par: Chen, Wei-Ning, et autres
Publié: (2024)
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
par: Zhao, Puning, et autres
Publié: (2024)
par: Zhao, Puning, et autres
Publié: (2024)
Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations
par: Hong, Sanghyun, et autres
Publié: (2025)
par: Hong, Sanghyun, et autres
Publié: (2025)
Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
par: Lin, Minhua, et autres
Publié: (2024)
par: Lin, Minhua, et autres
Publié: (2024)
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
par: Jin, Richeng, et autres
Publié: (2023)
par: Jin, Richeng, et autres
Publié: (2023)
Setting $\varepsilon$ is not the Issue in Differential Privacy
par: Cyffers, Edwige
Publié: (2025)
par: Cyffers, Edwige
Publié: (2025)
Limits of Personalizing Differential Privacy Budgets
par: Cyffers, Edwige, et autres
Publié: (2026)
par: Cyffers, Edwige, et autres
Publié: (2026)
Laplace Transform Interpretation of Differential Privacy
par: Chourasia, Rishav, et autres
Publié: (2024)
par: Chourasia, Rishav, et autres
Publié: (2024)
Contrastive Explainable Clustering with Differential Privacy
par: Nguyen, Dung, et autres
Publié: (2024)
par: Nguyen, Dung, et autres
Publié: (2024)
PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization
par: Yang, Qin, et autres
Publié: (2025)
par: Yang, Qin, et autres
Publié: (2025)
Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy
par: Saxena, Aman, et autres
Publié: (2026)
par: Saxena, Aman, et autres
Publié: (2026)
Differential Privacy Mechanisms in Neural Tangent Kernel Regression
par: Gu, Jiuxiang, et autres
Publié: (2024)
par: Gu, Jiuxiang, et autres
Publié: (2024)
Social-Aware Clustered Federated Learning with Customized Privacy Preservation
par: Wang, Yuntao, et autres
Publié: (2022)
par: Wang, Yuntao, et autres
Publié: (2022)
Privacy Amplification for Matrix Mechanisms
par: Choquette-Choo, Christopher A., et autres
Publié: (2023)
par: Choquette-Choo, Christopher A., et autres
Publié: (2023)
Differentially Private Range Queries with Correlated Input Perturbation
par: Dharangutte, Prathamesh, et autres
Publié: (2024)
par: Dharangutte, Prathamesh, et autres
Publié: (2024)
Optimal Fairness under Local Differential Privacy
par: Ghoukasian, Hrad, et autres
Publié: (2025)
par: Ghoukasian, Hrad, et autres
Publié: (2025)
Differential Privacy in the Extensive-Form Bandit Problem
par: Pasteris, Stephen, et autres
Publié: (2026)
par: Pasteris, Stephen, et autres
Publié: (2026)
Documents similaires
-
ObCLIP: Oblivious CLoud-Device Hybrid Image Generation with Privacy Preservation
par: Wu, Haoqi, et autres
Publié: (2025) -
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
par: Redberg, Rachel, et autres
Publié: (2023) -
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
par: Wei, Yuecen, et autres
Publié: (2023) -
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence
par: Feng, Shuya, et autres
Publié: (2024) -
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates
par: Wang, Chuanyin, et autres
Publié: (2025)