:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Luo, Chenxiang, Yau, David K. Y., Song, Qun
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Machine Learning
Online Access:	https://arxiv.org/abs/2510.03319
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Invisible Backdoor Attack Through Singular Value Decomposition
by: Chen, Wenmin, et al.
Published: (2024)

CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling
by: Zhang, Kaiyuan, et al.
Published: (2025)

Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning
by: Luo, Xinjian, et al.
Published: (2020)

Evaluating Selective Encryption Against Gradient Inversion Attacks
by: Gu, Jiajun, et al.
Published: (2025)

A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
by: elShehaby, Mohamed, et al.
Published: (2026)

Combining Stochastic Defenses to Resist Gradient Inversion: An Ablation Study
by: Scheliga, Daniel, et al.
Published: (2022)

Trap-MID: Trapdoor-based Defense against Model Inversion Attacks
by: Liu, Zhen-Ting, et al.
Published: (2024)

A New Federated Learning Framework Against Gradient Inversion Attacks
by: Guo, Pengxin, et al.
Published: (2024)

AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks
by: Zeng, Yifan, et al.
Published: (2024)

Optimal Defenses Against Gradient Reconstruction Attacks
by: Chen, Yuxiao, et al.
Published: (2024)

Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks
by: Xue, Lulu, et al.
Published: (2024)

Dashed Line Defense: Plug-And-Play Defense Against Adaptive Score-Based Query Attacks
by: Fu, Yanzhang, et al.
Published: (2026)

Practical Feasibility of Gradient Inversion Attacks in Federated Learning
by: Valadi, Viktor, et al.
Published: (2025)

ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
by: Gong, Zirui, et al.
Published: (2026)

A Data-Driven Defense against Edge-case Model Poisoning Attacks on Federated Learning
by: Purohit, Kiran, et al.
Published: (2023)

Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
by: Fan, Mingyuan, et al.
Published: (2022)

Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning
by: Shan, Junjie, et al.
Published: (2024)

Understanding Deep Gradient Leakage via Inversion Influence Functions
by: Zhang, Haobo, et al.
Published: (2023)

TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic via Asymmetric Contrastive Learning
by: Sun, Bowen, et al.
Published: (2026)

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks
by: Papadopoulos, Georgios, et al.
Published: (2025)

No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning
by: Diana, Francesco, et al.
Published: (2026)

Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
by: Obioma, Chibueze Peace, et al.
Published: (2025)

Attack and Defense of Deep Learning Models in the Field of Web Attack Detection
by: Shi, Lijia, et al.
Published: (2024)

Synthetic Tabular Data: Methods, Attacks and Defenses
by: Cormode, Graham, et al.
Published: (2025)

A Taxonomy of Attacks and Defenses in Split Learning
by: Shabbir, Aqsa, et al.
Published: (2025)

GI-PIP: Do We Require Impractical Auxiliary Dataset for Gradient Inversion Attacks?
by: Sun, Yu, et al.
Published: (2024)

Model Inversion Attacks Meet Cryptographic Fuzzy Extractors
by: Prabhakar, Mallika, et al.
Published: (2025)

Sponge Attacks on Sensing AI: Energy-Latency Vulnerabilities and Defense via Model Pruning
by: Hasan, Syed Mhamudul, et al.
Published: (2025)

SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding
by: Park, Chanho, et al.
Published: (2024)

Data Reconstruction Attacks and Defenses: A Systematic Evaluation
by: Liu, Sheng, et al.
Published: (2024)

Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks
by: Wang, Yanbo, et al.
Published: (2024)

Ensembler: Protect Collaborative Inference Privacy from Model Inversion Attack via Selective Ensemble
by: Liu, Dancheng, et al.
Published: (2024)

Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents
by: Zhan, Qiusi, et al.
Published: (2025)

FedGIG: Graph Inversion from Gradient in Federated Learning
by: Xiao, Tianzhe, et al.
Published: (2024)

Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
by: Zhang, Chenxiang, et al.
Published: (2025)

Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning
by: Zhang, Yiwei, et al.
Published: (2024)

PECAN: A Deterministic Certified Defense Against Backdoor Attacks
by: Zhang, Yuhao, et al.
Published: (2023)

Feature Inference Attack on Shapley Values
by: Luo, Xinjian, et al.
Published: (2024)

SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning Attacks
by: Paracha, Anum, et al.
Published: (2025)

Jailbreaking LLMs Without Gradients or Priors: Effective and Transferable Attacks
by: Nurlanov, Zhakshylyk, et al.
Published: (2026)