Image de couverture de livre

Towards Privacy-Preserving Split Learning: Destabilizing Adversarial Inference and Reconstruction Attacks in the Cloud

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Higgins, Griffin, Razavi-Far, Roozbeh, Zhang, Xichen, David, Amir, Ghorbani, Ali, Ge, Tongyu
Format:	Preprint
Publié:	2025
Sujets:	Cryptography and Security
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation
par: Shokouhinejad, Hossein, et autres
Publié: (2025)

Transparent Malware Detection With Granular Assembly Flow Explainability via Graph Neural Networks
par: Higgins, Griffin, et autres
Publié: (2026)

Explainable Attention-Guided Stacked Graph Neural Networks for Malware Detection
par: Shokouhinejad, Hossein, et autres
Publié: (2025)

Routing-Aware Explanations for Mixture of Experts Graph Models in Malware Detection
par: Shokouhinejad, Hossein, et autres
Publié: (2026)

Dual Explanations via Subgraph Matching for Malware Detection
par: Shokouhinejad, Hossein, et autres
Publié: (2025)

Explainable Malware Detection through Integrated Graph Reduction and Learning Techniques
par: Mohammadian, Hesamodin, et autres
Publié: (2024)

On the Consistency of GNN Explanations for Malware Detection
par: Shokouhinejad, Hossein, et autres
Publié: (2025)

Recent Advances in Malware Detection: Graph Learning and Explainability
par: Shokouhinejad, Hossein, et autres
Publié: (2025)

Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering
par: Jelodar, Hamed, et autres
Publié: (2025)

Decentralized Federated Learning: A Survey on Security and Privacy
par: Hallaji, Ehsan, et autres
Publié: (2024)

LLM Security and Safety: Insights from Homotopy-Inspired Prompt Obfuscation
par: Lazo, Luis, et autres
Publié: (2026)

A Study on the Importance of Features in Detecting Advanced Persistent Threats Using Machine Learning
par: Hallaji, Ehsan, et autres
Publié: (2025)

TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning
par: Hallaji, Ehsan, et autres
Publié: (2025)

A Study on Semi-Supervised Detection of DDoS Attacks under Class Imbalance
par: Hallaji, Ehsan, et autres
Publié: (2025)

FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL
par: Hallaji, Ehsan, et autres
Publié: (2025)

Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Models
par: Bai, Samita, et autres
Publié: (2026)

LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering
par: Jelodar, Hamed, et autres
Publié: (2026)

Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
par: Razavi-Far, Roozbeh, et autres
Publié: (2026)

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
par: Xue, Jing, et autres
Publié: (2025)

Passive Inference Attacks on Split Learning via Adversarial Regularization
par: Zhu, Xiaochen, et autres
Publié: (2023)

Learning-based Privacy-Preserving Graph Publishing Against Sensitive Link Inference Attacks
par: Wu, Yucheng, et autres
Publié: (2025)

CURE: Privacy-Preserving Split Learning Done Right
par: Kanpak, Halil Ibrahim, et autres
Publié: (2024)

OnePath: Efficient and Privacy-Preserving Decision Tree Inference in the Cloud
par: Yuan, Shuai, et autres
Publié: (2024)

Privacy-Preserving for Images in Satellite Communications: A Comprehensive Review of Chaos-Based Encryption
par: Rashid, Farrukh Bin, et autres
Publié: (2024)

Towards Split Learning-based Privacy-Preserving Record Linkage
par: Zervas, Michail, et autres
Publié: (2024)

RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation
par: Pham, Dzung, et autres
Publié: (2023)

The Communication-Friendly Privacy-Preserving Machine Learning against Malicious Adversaries
par: Lu, Tianpei, et autres
Publié: (2024)

SplitAgent: A Privacy-Preserving Distributed Architecture for Enterprise-Cloud Agent Collaboration
par: She, Jianshu
Publié: (2026)

Efficient and Verifiable Privacy-Preserving Convolutional Computation for CNN Inference with Untrusted Clouds
par: Lu, Jinyu, et autres
Publié: (2025)

A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning
par: Xu, Xiaoyang, et autres
Publié: (2024)

Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks
par: Noorbakhsh, Sayedeh Leila, et autres
Publié: (2024)

Microbenchmarking Cloud Cryptographic Workloads for Privacy-Preserving Healthcare IoT
par: Webb, Jeremiah L., et autres
Publié: (2026)

Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability
par: Sahani, Mukesh, et autres
Publié: (2025)

Privacy-Preserving Federated Learning Scheme with Mitigating Model Poisoning Attacks: Vulnerabilities and Countermeasures
par: Wu, Jiahui, et autres
Publié: (2025)

Design and Optimization of Cloud Native Homomorphic Encryption Workflows for Privacy-Preserving ML Inference
par: Bollikonda, Tejaswini
Publié: (2025)

Towards Privacy-Preserving Federated Learning using Hybrid Homomorphic Encryption
par: Costa, Ivan, et autres
Publié: (2026)

SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection
par: Li, Yekai, et autres
Publié: (2024)

Mining Power Destruction Attacks in the Presence of Petty-Compliant Mining Pools
par: Sarenche, Roozbeh, et autres
Publié: (2025)

Proteus: A Practical Framework for Privacy-Preserving Device Logs
par: Goutam, Sanket, et autres
Publié: (2026)

Revisiting the Privacy Risks of Split Inference: A GAN-Based Data Reconstruction Attack via Progressive Feature Optimization
par: Qiu, Yixiang, et autres
Publié: (2025)