Cover Image

Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Khan, Tanveer, Budzys, Mindaugas, Michalas, Antonis
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learning
by: Khan, Tanveer, et al.
Published: (2024)

GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption
by: Frimpong, Eugene, et al.
Published: (2024)

Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training
by: Khan, Tanveer, et al.
Published: (2024)

A Pervasive, Efficient and Private Future: Realizing Privacy-Preserving Machine Learning Through Hybrid Homomorphic Encryption
by: Nguyen, Khoa, et al.
Published: (2024)

Hidden Elo: Private Matchmaking through Encrypted Rating Systems
by: Budzys, Mindaugas, et al.
Published: (2026)

A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption
by: Nguyen, Khoa, et al.
Published: (2025)

Oops!... They Stole it Again: Attacks on Split Learning
by: Khan, Tanveer, et al.
Published: (2025)

Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability
by: Sahani, Mukesh, et al.
Published: (2025)

SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection
by: Erdogan, Ege, et al.
Published: (2023)

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

Passive Inference Attacks on Split Learning via Adversarial Regularization
by: Zhu, Xiaochen, et al.
Published: (2023)

An Efficient Privacy-aware Split Learning Framework for Satellite Communications
by: Sun, Jianfei, et al.
Published: (2024)

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training
by: Zmushko, Philip, et al.
Published: (2024)

Enhancing Privacy in ControlNet and Stable Diffusion via Split Learning
by: Yao, Dixi
Published: (2024)

Data Reconstruction: Identifiability and Optimization with Sample Splitting
by: Shen, Yujie, et al.
Published: (2026)

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

SecureSplit: Mitigating Backdoor Attacks in Split Learning
by: Dou, Zhihao, et al.
Published: (2026)

Towards Split Learning-based Privacy-Preserving Record Linkage
by: Zervas, Michail, et al.
Published: (2024)

Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation
by: Maddock, Samuel, et al.
Published: (2025)

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

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

FL-Defender: Combating Targeted Attacks in Federated Learning
by: Jebreel, Najeeb, et al.
Published: (2022)

Advancing Autonomous Incident Response: Leveraging LLMs and Cyber Threat Intelligence
by: Tellache, Amine, et al.
Published: (2025)

GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection
by: Rabooki, Saba Fathi, et al.
Published: (2025)

Byzantine-Robust Federated Learning Framework with Post-Quantum Secure Aggregation for Real-Time Threat Intelligence Sharing in Critical IoT Infrastructure
by: Rahmati, Milad, et al.
Published: (2026)

Point Intervention: Improving ACVP Test Vector Generation Through Human Assisted Fuzzing
by: Gridin, Iaroslav, et al.
Published: (2024)

Similarity-based Label Inference Attack against Training and Inference of Split Learning
by: Liu, Junlin, et al.
Published: (2022)

Survey of Privacy Threats and Countermeasures in Federated Learning
by: Hayashitani, Masahiro, et al.
Published: (2024)

SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)
by: Rieger, Phillip, et al.
Published: (2025)

An End-to-End Framework for Functionality-Embedded Provenance Graph Construction and Threat Interpretation
by: Ghosh, Kushankur, et al.
Published: (2026)

Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning
by: Shankar, Aditya, et al.
Published: (2024)

Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning
by: Basnet, Animesh Singh, et al.
Published: (2024)

Split-State Non-Malleable Codes and Secret Sharing Schemes for Quantum Messages
by: Boddu, Naresh Goud, et al.
Published: (2023)

Combating Advanced Persistent Threats: Challenges and Solutions
by: Wang, Yuntao, et al.
Published: (2023)

LPUF-AuthNet: A Lightweight PUF-Based IoT Authentication via Tandem Neural Networks and Split Learning
by: Mefgouda, Brahim, et al.
Published: (2024)

Sigil: Server-Enforced Watermarking in U-Shaped Split Federated Learning via Gradient Injection
by: Dai, Zhengchunmin, et al.
Published: (2025)

Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation
by: Jiang, Yukun, et al.
Published: (2024)

EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection
by: Qian, Yuwen, et al.
Published: (2024)

A Data-Driven Predictive Analysis on Cyber Security Threats with Key Risk Factors
by: Johora, Fatama Tuz, et al.
Published: (2024)

FRAME : Comprehensive Risk Assessment Framework for Adversarial Machine Learning Threats
by: Shapira, Avishag, et al.
Published: (2025)