Cover Image

DP-GPL: Differentially Private Graph Prompt Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Jing, Boenisch, Franziska, Olatunji, Iyiola Emmanuel, Dziedzic, Adam
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
by: Hanke, Vincent, et al.
Published: (2024)

Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs)
by: Olatunji, Iyiola E., et al.
Published: (2025)

Differentially Private Prototypes for Imbalanced Transfer Learning
by: Wahdany, Dariush, et al.
Published: (2024)

Memorization in Graph Neural Networks
by: Jamadandi, Adarsh, et al.
Published: (2025)

ADAGE: Active Defenses Against GNN Extraction
by: Xu, Jing, et al.
Published: (2025)

Differentially Private Federated Learning With Time-Adaptive Privacy Spending
by: Kiani, Shahrzad, et al.
Published: (2025)

Have it your way: Individualized Privacy Assignment for DP-SGD
by: Boenisch, Franziska, et al.
Published: (2023)

Localizing and Mitigating Memorization in Image Autoregressive Models
by: Kasliwal, Aditya, et al.
Published: (2025)

Curation Leaks: Membership Inference Attacks against Data Curation for Machine Learning
by: Wahdany, Dariush, et al.
Published: (2026)

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
by: Wang, Xun, et al.
Published: (2025)

Unlocking Post-hoc Dataset Inference with Synthetic Data
by: Zhao, Bihe, et al.
Published: (2025)

CDI: Copyrighted Data Identification in Diffusion Models
by: Dubiński, Jan, et al.
Published: (2024)

On the Privacy Risk of In-context Learning
by: Duan, Haonan, et al.
Published: (2024)

Implementing Adaptations for Vision AutoRegressive Model
by: Shaikh, Kaif, et al.
Published: (2025)

Radioactive Watermarks in Diffusion and Autoregressive Image Generative Models
by: Meintz, Michel, et al.
Published: (2025)

Privacy Attacks on Image AutoRegressive Models
by: Kowalczuk, Antoni, et al.
Published: (2025)

Localizing Memorization in SSL Vision Encoders
by: Wang, Wenhao, et al.
Published: (2024)

Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
by: Podhajski, Marcin, et al.
Published: (2024)

On Stealing Graph Neural Network Models
by: Podhajski, Marcin, et al.
Published: (2025)

Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models
by: Hintersdorf, Dominik, et al.
Published: (2024)

Memorization in Self-Supervised Learning Improves Downstream Generalization
by: Wang, Wenhao, et al.
Published: (2024)

MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation
by: Wang, Yihan, et al.
Published: (2024)

Captured by Captions: On Memorization and its Mitigation in CLIP Models
by: Wang, Wenhao, et al.
Published: (2025)

Finding DoRI: Discovery of Retained Images in Diffusion Models
by: Kowalczuk, Antoni, et al.
Published: (2025)

Personalized Differential Privacy for Ridge Regression
by: Acharya, Krishna, et al.
Published: (2024)

DP-HYPE: Distributed Differentially Private Hyperparameter Search
by: Liebenow, Johannes, et al.
Published: (2025)

FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations
by: Wang, Hui-Po, et al.
Published: (2023)

DP-LDMs: Differentially Private Latent Diffusion Models
by: Liu, Michael F., et al.
Published: (2023)

DP-KAN: Differentially Private Kolmogorov-Arnold Networks
by: Kalinin, Nikita P., et al.
Published: (2024)

FusionDP: Foundation Model-Assisted Differentially Private Learning for Partially Sensitive Features
by: Zeng, Linghui, et al.
Published: (2025)

DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication
by: Zhu, Zehan, et al.
Published: (2025)

DP-Muon: Differentially Private Optimization via Matrix-Orthogonalized Momentum
by: Kim, Jihwan, et al.
Published: (2026)

DP-TabICL: In-Context Learning with Differentially Private Tabular Data
by: Carey, Alycia N., et al.
Published: (2024)

DP-TLDM: Differentially Private Tabular Latent Diffusion Model
by: Zhu, Chaoyi, et al.
Published: (2024)

DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training
by: Kalinin, Nikita P., et al.
Published: (2026)

Regulation Games for Trustworthy Machine Learning
by: Yaghini, Mohammad, et al.
Published: (2024)

DP-SPRT: Differentially Private Sequential Probability Ratio Tests
by: Michel, Thomas, et al.
Published: (2025)

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
by: Gao, Fengyu, et al.
Published: (2024)

DP-OPD: Differentially Private On-Policy Distillation for Language Models
by: Khadem, Fatemeh, et al.
Published: (2026)

Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
by: Ngo, Hoang M., et al.
Published: (2026)