Imagen de Portada

Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhang, Xiaojin, Chen, Wei
Formato:	Preprint
Publicado:	2024
Materias:	Cryptography and Security Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Bridging Privacy and Robustness for Trustworthy Machine Learning
por: Zhang, Xiaojin, et al.
Publicado: (2024)

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

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

Evaluating Privacy Leakage in Split Learning
por: Qiu, Xinchi, et al.
Publicado: (2023)

A Game-theoretic Framework for Privacy-preserving Federated Learning
por: Zhang, Xiaojin, et al.
Publicado: (2023)

A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
por: Yu, Lei, et al.
Publicado: (2024)

Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective
por: Wang, Zifan, et al.
Publicado: (2024)

Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
por: Xu, Yukai, et al.
Publicado: (2024)

CTIGuardian: A Few-Shot Framework for Mitigating Privacy Leakage in Fine-Tuned LLMs
por: Arachchige, Shashie Dilhara Batan, et al.
Publicado: (2025)

Decentralized Federated Learning: A Survey on Security and Privacy
por: Hallaji, Ehsan, et al.
Publicado: (2024)

Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
por: Piran, Fardin Jalil, et al.
Publicado: (2024)

Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
por: Zhang, Xiaojin, et al.
Publicado: (2025)

Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
por: Rashid, Md Rafi Ur, et al.
Publicado: (2024)

LeakAgent: RL-based Red-teaming Agent for LLM Privacy Leakage
por: Nie, Yuzhou, et al.
Publicado: (2024)

Privacy-Preserving in Blockchain-based Federated Learning Systems
por: M., Sameera K., et al.
Publicado: (2024)

Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
por: Scheliga, Daniel, et al.
Publicado: (2023)

Federated Split Learning for Human Activity Recognition with Differential Privacy
por: Ndeko, Josue, et al.
Publicado: (2024)

Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
por: Dong, Wenhan, et al.
Publicado: (2024)

Privacy Assessment of Federated Learning using Private Personalized Layers
por: Jourdan, Théo, et al.
Publicado: (2021)

Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning
por: Lee, Joohyung, et al.
Publicado: (2023)

Cross-silo Federated Learning with Record-level Personalized Differential Privacy
por: Liu, Junxu, et al.
Publicado: (2024)

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
por: Hiniduma, Kaveen, et al.
Publicado: (2025)

Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
por: Khalil, Mohammad, et al.
Publicado: (2025)

Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning
por: Németh, Gergely Dániel, et al.
Publicado: (2023)

Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
por: Yu, Sixing, et al.
Publicado: (2023)

Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials
por: Tertulino, Rodrigo, et al.
Publicado: (2026)

Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
por: Diana, Francesco, et al.
Publicado: (2025)

Trustworthy AI: Safety, Bias, and Privacy -- A Survey
por: Fang, Xingli, et al.
Publicado: (2025)

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

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

Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective
por: Luo, Xinjian, et al.
Publicado: (2024)

EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage
por: Liao, Zeyi, et al.
Publicado: (2024)

Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings
por: Arazzi, Marco, et al.
Publicado: (2025)

A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain
por: Wang, Junfei, et al.
Publicado: (2024)

Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey
por: Jia, Yan, et al.
Publicado: (2024)

A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
por: Shao, Jiawei, et al.
Publicado: (2023)

Analysis of Privacy Leakage in Federated Large Language Models
por: Vu, Minh N., et al.
Publicado: (2024)

BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks
por: Yadav, Sushilkumar, et al.
Publicado: (2024)

Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning
por: Zhang, Yujie, et al.
Publicado: (2024)

LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
por: Rokvic, Ljubomir, et al.
Publicado: (2022)