Guardado en:
| Autores principales: | Chaudhuri, Syomantak, Courtade, Thomas A. |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2509.02856 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Private Estimation when Data and Privacy Demands are Correlated
por: Chaudhuri, Syomantak, et al.
Publicado: (2024)
por: Chaudhuri, Syomantak, et al.
Publicado: (2024)
Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
por: Aliakbarpour, Maryam, et al.
Publicado: (2024)
por: Aliakbarpour, Maryam, et al.
Publicado: (2024)
Auditing $f$-Differential Privacy in One Run
por: Mahloujifar, Saeed, et al.
Publicado: (2024)
por: Mahloujifar, Saeed, et al.
Publicado: (2024)
Machine Learning with Privacy for Protected Attributes
por: Mahloujifar, Saeed, et al.
Publicado: (2025)
por: Mahloujifar, Saeed, et al.
Publicado: (2025)
Privacy Amplification for the Gaussian Mechanism via Bounded Support
por: Hu, Shengyuan, et al.
Publicado: (2024)
por: Hu, Shengyuan, et al.
Publicado: (2024)
Robust Estimation Under Heterogeneous Corruption Rates
por: Chaudhuri, Syomantak, et al.
Publicado: (2025)
por: Chaudhuri, Syomantak, et al.
Publicado: (2025)
A User-Centric, Privacy-Preserving, and Verifiable Ecosystem for Personal Data Management and Utilization
por: Zafar, Osama, et al.
Publicado: (2025)
por: Zafar, Osama, et al.
Publicado: (2025)
Privacy Amplification for Matrix Mechanisms
por: Choquette-Choo, Christopher A., et al.
Publicado: (2023)
por: Choquette-Choo, Christopher A., et al.
Publicado: (2023)
Diverse Community Data for Benchmarking Data Privacy Algorithms
por: Sen, Aniruddha, et al.
Publicado: (2023)
por: Sen, Aniruddha, et al.
Publicado: (2023)
Data Privacy Preservation on the Internet of Things
por: Sen, Jaydip, et al.
Publicado: (2023)
por: Sen, Jaydip, et al.
Publicado: (2023)
DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy
por: Wang, Erchi, et al.
Publicado: (2026)
por: Wang, Erchi, et al.
Publicado: (2026)
Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy
por: Ganev, Georgi, et al.
Publicado: (2025)
por: Ganev, Georgi, et al.
Publicado: (2025)
Privacy-Preserving Fair Synthetic Tabular Data
por: Sarmin, Fatima J., et al.
Publicado: (2025)
por: Sarmin, Fatima J., et al.
Publicado: (2025)
Privacy Vulnerabilities in Marginals-based Synthetic Data
por: Golob, Steven, et al.
Publicado: (2024)
por: Golob, Steven, et al.
Publicado: (2024)
Training Data Reconstruction: Privacy due to Uncertainty?
por: Runkel, Christina, et al.
Publicado: (2024)
por: Runkel, Christina, et al.
Publicado: (2024)
Leveraging Randomness in Model and Data Partitioning for Privacy Amplification
por: Dong, Andy, et al.
Publicado: (2025)
por: Dong, Andy, et al.
Publicado: (2025)
Does Training with Synthetic Data Truly Protect Privacy?
por: Zhao, Yunpeng, et al.
Publicado: (2025)
por: Zhao, Yunpeng, et al.
Publicado: (2025)
Synthetic Data: Revisiting the Privacy-Utility Trade-off
por: Sarmin, Fatima Jahan, et al.
Publicado: (2024)
por: Sarmin, Fatima Jahan, et al.
Publicado: (2024)
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
por: Feng, Shanglun, et al.
Publicado: (2024)
por: Feng, Shanglun, et al.
Publicado: (2024)
PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces
por: Wong, Wan Ki, et al.
Publicado: (2025)
por: Wong, Wan Ki, et al.
Publicado: (2025)
Privacy-Preserving Analytics for Smart Meter (AMI) Data: A Hybrid Approach to Comply with CPUC Privacy Regulations
por: Westrich, Benjamin
Publicado: (2025)
por: Westrich, Benjamin
Publicado: (2025)
Can We Infer Confidential Properties of Training Data from LLMs?
por: Huang, Pengrun, et al.
Publicado: (2025)
por: Huang, Pengrun, et al.
Publicado: (2025)
SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
por: Zhou, Yan, et al.
Publicado: (2025)
por: Zhou, Yan, et al.
Publicado: (2025)
Learning to Attack: Uncovering Privacy Risks in Sequential Data Releases
por: Cui, Ziyao, et al.
Publicado: (2025)
por: Cui, Ziyao, et al.
Publicado: (2025)
Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data
por: Hsu, I-Jung, et al.
Publicado: (2025)
por: Hsu, I-Jung, et al.
Publicado: (2025)
Privacy-Preserving Statistical Data Generation: Application to Sepsis Detection
por: Macias-Fassio, Eric, et al.
Publicado: (2024)
por: Macias-Fassio, Eric, et al.
Publicado: (2024)
Distributed Deep Variational Approach for Privacy-preserving Data Release
por: Alsulaimawi, Zahir, et al.
Publicado: (2026)
por: Alsulaimawi, Zahir, et al.
Publicado: (2026)
Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Web
por: Ramesh, Vishal, et al.
Publicado: (2023)
por: Ramesh, Vishal, et al.
Publicado: (2023)
Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models
por: Ward, Joshua, et al.
Publicado: (2024)
por: Ward, Joshua, et al.
Publicado: (2024)
SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy
por: Nahid, Md Mahadi Hasan, et al.
Publicado: (2024)
por: Nahid, Md Mahadi Hasan, et al.
Publicado: (2024)
Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
por: Hod, Shlomi, et al.
Publicado: (2025)
por: Hod, Shlomi, et al.
Publicado: (2025)
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation
por: Vithana, Sajani, et al.
Publicado: (2024)
por: Vithana, Sajani, et al.
Publicado: (2024)
SPICED: Syntactical Bug and Trojan Pattern Identification in A/MS Circuits using LLM-Enhanced Detection
por: Chaudhuri, Jayeeta, et al.
Publicado: (2024)
por: Chaudhuri, Jayeeta, et al.
Publicado: (2024)
Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
por: Schneider, Thomas, et al.
Publicado: (2024)
por: Schneider, Thomas, et al.
Publicado: (2024)
Privacy Amplification Through Synthetic Data: Insights from Linear Regression
por: Pierquin, Clément, et al.
Publicado: (2025)
por: Pierquin, Clément, et al.
Publicado: (2025)
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
por: Fang, Congyu, et al.
Publicado: (2024)
por: Fang, Congyu, et al.
Publicado: (2024)
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
por: Schwethelm, Kristian, et al.
Publicado: (2024)
por: Schwethelm, Kristian, et al.
Publicado: (2024)
Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue
por: Brunner, David, et al.
Publicado: (2024)
por: Brunner, David, et al.
Publicado: (2024)
SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AI
por: Giddens, Spencer, et al.
Publicado: (2024)
por: Giddens, Spencer, et al.
Publicado: (2024)
Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data
por: Akkus, Atilla, et al.
Publicado: (2024)
por: Akkus, Atilla, et al.
Publicado: (2024)
Ejemplares similares
-
Private Estimation when Data and Privacy Demands are Correlated
por: Chaudhuri, Syomantak, et al.
Publicado: (2024) -
Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
por: Aliakbarpour, Maryam, et al.
Publicado: (2024) -
Auditing $f$-Differential Privacy in One Run
por: Mahloujifar, Saeed, et al.
Publicado: (2024) -
Machine Learning with Privacy for Protected Attributes
por: Mahloujifar, Saeed, et al.
Publicado: (2025) -
Privacy Amplification for the Gaussian Mechanism via Bounded Support
por: Hu, Shengyuan, et al.
Publicado: (2024)