On the Query Complexity of Training Data Reconstruction in Private Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Mukherjee, Prateeti, Lokam, Satya |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SLIP: Securing LLMs IP Using Weights Decomposition
by: Refael, Yehonathan, et al.
Published: (2024)
by: Refael, Yehonathan, et al.
Published: (2024)
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols
by: Liao, Ting-Wei, et al.
Published: (2025)
by: Liao, Ting-Wei, et al.
Published: (2025)
Private Training & Data Generation by Clustering Embeddings
by: Zhou, Felix, et al.
Published: (2025)
by: Zhou, Felix, et al.
Published: (2025)
Training Set Reconstruction from Differentially Private Forests: How Effective is DP?
by: Gorgé, Alice, et al.
Published: (2025)
by: Gorgé, Alice, et al.
Published: (2025)
DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
by: Liu, Yixuan, et al.
Published: (2024)
by: Liu, Yixuan, et al.
Published: (2024)
Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private
by: Wu, Ruihan, et al.
Published: (2025)
by: Wu, Ruihan, et al.
Published: (2025)
Training Data Reconstruction: Privacy due to Uncertainty?
by: Runkel, Christina, et al.
Published: (2024)
by: Runkel, Christina, et al.
Published: (2024)
Oracle-Efficient Differentially Private Learning with Public Data
by: Block, Adam, et al.
Published: (2024)
by: Block, Adam, et al.
Published: (2024)
Differentially Private Clustering in Data Streams
by: Epasto, Alessandro, et al.
Published: (2023)
by: Epasto, Alessandro, et al.
Published: (2023)
SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models
by: Zhang, Jiang, et al.
Published: (2025)
by: Zhang, Jiang, et al.
Published: (2025)
Differentially Private Range Queries with Correlated Input Perturbation
by: Dharangutte, Prathamesh, et al.
Published: (2024)
by: Dharangutte, Prathamesh, et al.
Published: (2024)
Graph Reconstruction from Differentially Private GNN Explanations
by: Sahoo, Rishi Raj, et al.
Published: (2026)
by: Sahoo, Rishi Raj, et al.
Published: (2026)
Differentially Private Training of Mixture of Experts Models
by: Tholoniat, Pierre, et al.
Published: (2024)
by: Tholoniat, Pierre, et al.
Published: (2024)
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
by: Zhou, Yvonne, et al.
Published: (2024)
by: Zhou, Yvonne, et al.
Published: (2024)
Training Differentially Private Models with Secure Multiparty Computation
by: Pentyala, Sikha, et al.
Published: (2022)
by: Pentyala, Sikha, et al.
Published: (2022)
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
by: Schwethelm, Kristian, et al.
Published: (2024)
by: Schwethelm, Kristian, et al.
Published: (2024)
Privately Learning Decision Lists and a Differentially Private Winnow
by: Bun, Mark, et al.
Published: (2026)
by: Bun, Mark, et al.
Published: (2026)
Optimal Differentially Private Model Training with Public Data
by: Lowy, Andrew, et al.
Published: (2023)
by: Lowy, Andrew, et al.
Published: (2023)
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
by: He, Longzhu, et al.
Published: (2025)
by: He, Longzhu, et al.
Published: (2025)
Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
by: Zhang, Meifan, et al.
Published: (2024)
by: Zhang, Meifan, et al.
Published: (2024)
On the Gradient Complexity of Private Optimization with Private Oracles
by: Menart, Michael, et al.
Published: (2025)
by: Menart, Michael, et al.
Published: (2025)
Banded Square Root Matrix Factorization for Differentially Private Model Training
by: Kalinin, Nikita P., et al.
Published: (2024)
by: Kalinin, Nikita P., et al.
Published: (2024)
Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent
by: Dufraiche, Jean, et al.
Published: (2026)
by: Dufraiche, Jean, et al.
Published: (2026)
Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
by: Guo, Wenkai, et al.
Published: (2025)
by: Guo, Wenkai, et al.
Published: (2025)
DPSR: Differentially Private Sparse Reconstruction via Multi-Stage Denoising for Recommender Systems
by: Ali, Sarwan
Published: (2025)
by: Ali, Sarwan
Published: (2025)
FlashDP: Private Training Large Language Models with Efficient DP-SGD
by: Wang, Liangyu, et al.
Published: (2025)
by: Wang, Liangyu, et al.
Published: (2025)
SelectFormer: Private and Practical Data Selection for Transformers
by: Ouyang, Xu, et al.
Published: (2023)
by: Ouyang, Xu, et al.
Published: (2023)
Optimal Locally Private Nonparametric Classification with Public Data
by: Ma, Yuheng, et al.
Published: (2023)
by: Ma, Yuheng, et al.
Published: (2023)
Samplable Anonymous Aggregation for Private Federated Data Analysis
by: Talwar, Kunal, et al.
Published: (2023)
by: Talwar, Kunal, et al.
Published: (2023)
Private Estimation when Data and Privacy Demands are Correlated
by: Chaudhuri, Syomantak, et al.
Published: (2024)
by: Chaudhuri, Syomantak, et al.
Published: (2024)
URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning
by: Yao, Duanyi, et al.
Published: (2024)
by: Yao, Duanyi, et al.
Published: (2024)
Federated Learning Nodes Can Reconstruct Peers' Image Data
by: Wilson, Ethan, et al.
Published: (2024)
by: Wilson, Ethan, et al.
Published: (2024)
Privately Aligning Language Models with Reinforcement Learning
by: Wu, Fan, et al.
Published: (2023)
by: Wu, Fan, et al.
Published: (2023)
HoGS: Homophily-Oriented Graph Synthesis for Local Differentially Private GNN Training
by: Xu, Wen, et al.
Published: (2026)
by: Xu, Wen, et al.
Published: (2026)
UTrace: Poisoning Forensics for Private Collaborative Learning
by: Rose, Evan, et al.
Published: (2024)
by: Rose, Evan, et al.
Published: (2024)
Correlated Noise Mechanisms for Differentially Private Learning
by: Pillutla, Krishna, et al.
Published: (2025)
by: Pillutla, Krishna, et al.
Published: (2025)
Differentially Private Release and Learning of Threshold Functions
by: Bun, Mark, et al.
Published: (2015)
by: Bun, Mark, et al.
Published: (2015)
Differentially Private Decentralized Learning with Random Walks
by: Cyffers, Edwige, et al.
Published: (2024)
by: Cyffers, Edwige, et al.
Published: (2024)
Attesting Distributional Properties of Training Data for Machine Learning
by: Duddu, Vasisht, et al.
Published: (2023)
by: Duddu, Vasisht, et al.
Published: (2023)
MongoDB Injection Query Classification Model using MongoDB Log files as Training Data
by: Perni, Shaunak, et al.
Published: (2026)
by: Perni, Shaunak, et al.
Published: (2026)
Similar Items
-
SLIP: Securing LLMs IP Using Weights Decomposition
by: Refael, Yehonathan, et al.
Published: (2024) -
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols
by: Liao, Ting-Wei, et al.
Published: (2025) -
Private Training & Data Generation by Clustering Embeddings
by: Zhou, Felix, et al.
Published: (2025) -
Training Set Reconstruction from Differentially Private Forests: How Effective is DP?
by: Gorgé, Alice, et al.
Published: (2025) -
DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
by: Liu, Yixuan, et al.
Published: (2024)