Copertina

Synthetic Data: Revisiting the Privacy-Utility Trade-off

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sarmin, Fatima Jahan, Sarkar, Atiquer Rahman, Wang, Yang, Mohammed, Noman
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Cryptography and Security Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Privacy-Preserving Fair Synthetic Tabular Data
di: Sarmin, Fatima J., et al.
Pubblicazione: (2025)

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
di: Zhou, Yan, et al.
Pubblicazione: (2025)

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
di: Inane, Ahmed Mehdi, et al.
Pubblicazione: (2026)

A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning
di: Zhang, Xiaojin, et al.
Pubblicazione: (2024)

Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation
di: Yin, Yi, et al.
Pubblicazione: (2025)

Mitigating Privacy-Utility Trade-off in Decentralized Federated Learning via $f$-Differential Privacy
di: Li, Xiang, et al.
Pubblicazione: (2025)

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
di: Li, Yuecheng, et al.
Pubblicazione: (2024)

A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
di: Kang, Yan, et al.
Pubblicazione: (2022)

Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy
di: Chen, Wei-Ning, et al.
Pubblicazione: (2024)

Revisiting Privacy-Utility Trade-off for DP Training with Pre-existing Knowledge
di: Zheng, Yu, et al.
Pubblicazione: (2024)

Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning
di: Ahamed, Sayyed Farid, et al.
Pubblicazione: (2024)

Privacy Vulnerabilities in Marginals-based Synthetic Data
di: Golob, Steven, et al.
Pubblicazione: (2024)

Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy
di: Ganev, Georgi, et al.
Pubblicazione: (2025)

Does Training with Synthetic Data Truly Protect Privacy?
di: Zhao, Yunpeng, et al.
Pubblicazione: (2025)

Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Web
di: Ramesh, Vishal, et al.
Pubblicazione: (2023)

The Privacy-Utility Trade-off in the Topics API
di: Alvim, Mário S., et al.
Pubblicazione: (2024)

SoK: Can Trajectory Generation Combine Privacy and Utility?
di: Buchholz, Erik, et al.
Pubblicazione: (2024)

Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs
di: Hannemann, Anika, et al.
Pubblicazione: (2024)

Optimizing Privacy-Utility Trade-off in Decentralized Learning with Generalized Correlated Noise
di: Rodio, Angelo, et al.
Pubblicazione: (2025)

PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces
di: Wong, Wan Ki, et al.
Pubblicazione: (2025)

Initial Exploration of Zero-Shot Privacy Utility Tradeoffs in Tabular Data Using GPT-4
di: Mandal, Bishwas, et al.
Pubblicazione: (2024)

Privacy Amplification Through Synthetic Data: Insights from Linear Regression
di: Pierquin, Clément, et al.
Pubblicazione: (2025)

Revisiting Hyperparameter Tuning with Differential Privacy
di: Ding, Youlong, et al.
Pubblicazione: (2022)

Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization
di: Mandal, Bishwas, et al.
Pubblicazione: (2024)

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
di: Ertan, Murat Bilgehan, et al.
Pubblicazione: (2026)

SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy
di: Nahid, Md Mahadi Hasan, et al.
Pubblicazione: (2024)

Revisiting Locally Differentially Private Protocols: Towards Better Trade-offs in Privacy, Utility, and Attack Resistance
di: Arcolezi, Héber H., et al.
Pubblicazione: (2025)

Bridging Privacy and Utility: Synthesizing anonymized EEG with constraining utility functions
di: Fuhrmeister, Kay, et al.
Pubblicazione: (2025)

Privacy Implications of Explainable AI in Data-Driven Systems
di: Ezzeddine, Fatima
Pubblicazione: (2024)

Optimizing the Privacy-Utility Balance using Synthetic Data and Configurable Perturbation Pipelines
di: Sharma, Anantha, et al.
Pubblicazione: (2025)

Privacy-Preserving Customer Churn Prediction Model in the Context of Telecommunication Industry
di: Sana, Joydeb Kumar, et al.
Pubblicazione: (2024)

Preempting Text Sanitization Utility in Resource-Constrained Privacy-Preserving LLM Interactions
di: Carpentier, Robin, et al.
Pubblicazione: (2024)

Driving Privacy Forward: Mitigating Information Leakage within Smart Vehicles through Synthetic Data Generation
di: Parikh, Krish
Pubblicazione: (2024)

An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses
di: Liang, Hao, et al.
Pubblicazione: (2025)

Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing
di: Islam, MD Shafikul, et al.
Pubblicazione: (2026)

The DCR Delusion: Measuring the Privacy Risk of Synthetic Data
di: Yao, Zexi, et al.
Pubblicazione: (2025)

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
di: Zhang, Xiaojin, et al.
Pubblicazione: (2023)

Jacobian-Guided Anisotropic Noise Reshaping for Enhancing Representation Utility under Local Differential Privacy
di: Ha, Youngmok, et al.
Pubblicazione: (2026)

Six Levels of Privacy: A Framework for Financial Synthetic Data
di: Balch, Tucker, et al.
Pubblicazione: (2024)

KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation
di: Kotal, Anantaa, et al.
Pubblicazione: (2024)