Copertina

OASIS: Offsetting Active Reconstruction Attacks in Federated Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Jeter, Tre' R., Nguyen, Truc, Alharbi, Raed, Thai, My T.
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Cryptography and Security Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models
di: Nguyen, Quan, et al.
Pubblicazione: (2025)

Denial-of-Service Vulnerability of Hash-based Transaction Sharding: Attack and Countermeasure
di: Nguyen, Truc, et al.
Pubblicazione: (2020)

Preserving Privacy and Security in Federated Learning
di: Nguyen, Truc, et al.
Pubblicazione: (2022)

Guaranteeing Privacy in Hybrid Quantum Learning through Theoretical Mechanisms
di: Ngo, Hoang M., et al.
Pubblicazione: (2026)

Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks
di: Alharbi, Ebtisaam, et al.
Pubblicazione: (2025)

Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method
di: Sahu, Abhijeet, et al.
Pubblicazione: (2024)

UIFV: Data Reconstruction Attack in Vertical Federated Learning
di: Yang, Jirui, et al.
Pubblicazione: (2024)

Local Model Reconstruction Attacks in Federated Learning and their Uses
di: Driouich, Ilias, et al.
Pubblicazione: (2022)

Manipulating LLM Web Agents with Indirect Prompt Injection Attack via HTML Accessibility Tree
di: Johnson, Sam, et al.
Pubblicazione: (2025)

FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
di: Han, Shanshan, et al.
Pubblicazione: (2023)

NOIR: Privacy-Preserving Generation of Code with Open-Source LLMs
di: Nguyen, Khoa, et al.
Pubblicazione: (2026)

Privacy Attack in Federated Learning is Not Easy: An Experimental Study
di: Zhu, Hangyu, et al.
Pubblicazione: (2024)

FragBench: Cross-Session Attacks Hidden in Benign-Looking Fragments
di: Mehta, Astha, et al.
Pubblicazione: (2026)

Selection-Based Vulnerabilities: Clean-Label Backdoor Attacks in Active Learning
di: Zhi, Yuhan, et al.
Pubblicazione: (2025)

Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
di: Kumar, K Naveen, et al.
Pubblicazione: (2024)

Federated Learning under Attack: Improving Gradient Inversion for Batch of Images
di: Leite, Luiz, et al.
Pubblicazione: (2024)

Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response
di: Ozturk, Ozer, et al.
Pubblicazione: (2025)

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
di: Carney, Jane, et al.
Pubblicazione: (2025)

GUIDE: Enhancing Gradient Inversion Attacks in Federated Learning with Denoising Models
di: Carletti, Vincenzo, et al.
Pubblicazione: (2025)

FedCC: Robust Federated Learning against Model Poisoning Attacks
di: Jeong, Hyejun, et al.
Pubblicazione: (2022)

Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
di: Xu, Runhua, et al.
Pubblicazione: (2025)

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

Approximate and Weighted Data Reconstruction Attack in Federated Learning
di: Song, Yongcun, et al.
Pubblicazione: (2023)

Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks
di: Guo, Pengxin, et al.
Pubblicazione: (2025)

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

Model Inversion Attack against Federated Unlearning
di: Zhou, Lei, et al.
Pubblicazione: (2025)

Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning
di: Li, Jiaxin, et al.
Pubblicazione: (2024)

Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
di: Xie, Chulin, et al.
Pubblicazione: (2022)

VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learning
di: Tan, Juntao, et al.
Pubblicazione: (2025)

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

False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning
di: Uddin, Md Raihan, et al.
Pubblicazione: (2024)

Rethinking Model Inversion Attacks With Patch-Wise Reconstruction
di: Jang, Jonggyu, et al.
Pubblicazione: (2023)

SAB:A Stealing and Robust Backdoor Attack based on Steganographic Algorithm against Federated Learning
di: Xu, Weida, et al.
Pubblicazione: (2024)

Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks
di: korba, Abdelaziz Amara, et al.
Pubblicazione: (2024)

Blockchains through ontologies: the case study of the Ethereum ERC721 standard in OASIS (Extended Version)
di: Bella, Giampaolo, et al.
Pubblicazione: (2021)

Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy
di: Shi, Yichuan, et al.
Pubblicazione: (2024)

Secure and Efficient UAV-Based Face Detection via Homomorphic Encryption and Edge Computing
di: Van Duc, Nguyen, et al.
Pubblicazione: (2025)

Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering
di: Lyu, Xingyu, et al.
Pubblicazione: (2025)

WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems
di: M., Sameera K., et al.
Pubblicazione: (2025)