:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Ioniţă, Alexandru, Ioniţă, Andreea
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Machine Learning
Online Access:	https://arxiv.org/abs/2509.21497
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Securing Transfer-Learned Networks with Reverse Homomorphic Encryption
by: Allison, Robert, et al.
Published: (2025)

PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security
by: Van Langendonck, Louis, et al.
Published: (2024)

Tracing Privacy Leakage of Language Models to Training Data via Adjusted Influence Functions
by: Liu, Jinxin, et al.
Published: (2024)

Driving Privacy Forward: Mitigating Information Leakage within Smart Vehicles through Synthetic Data Generation
by: Parikh, Krish
Published: (2024)

Defeating Cerberus: Concept-Guided Privacy-Leakage Mitigation in Multimodal Language Models
by: Zhang, Boyang, et al.
Published: (2025)

Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption
by: Biswas, Sayan, et al.
Published: (2025)

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

Practical, Private Assurance of the Value of Collaboration via Fully Homomorphic Encryption
by: Asghar, Hassan Jameel, et al.
Published: (2023)

Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy
by: Krüger, Fabian P., et al.
Published: (2024)

Nemesis: Noise-randomized Encryption with Modular Efficiency and Secure Integration in Machine Learning Systems
by: Zhao, Dongfang
Published: (2024)

Information Leakage from Data Updates in Machine Learning Models
by: Hui, Tian, et al.
Published: (2023)

Deep-Lock: Secure Authorization for Deep Neural Networks
by: Alam, Manaar, et al.
Published: (2020)

DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networks
by: Sui, Hao, et al.
Published: (2024)

Confidential Computing for Cloud Security: Exploring Hardware based Encryption Using Trusted Execution Environments
by: Agarwal, Dhruv Deepak, et al.
Published: (2025)

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

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

CryptGNN: Enabling Secure Inference for Graph Neural Networks
by: Sen, Pritam, et al.
Published: (2025)

Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
by: Yuan, Hanyang, et al.
Published: (2024)

Sanitize Your Responses: Mitigating Privacy Leakage in Large Language Models
by: Fu, Wenjie, et al.
Published: (2025)

MedLeak: Multimodal Medical Data Leakage in Secure Federated Learning with Crafted Models
by: Shi, Shanghao, et al.
Published: (2024)

Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
by: Ward, Joshua, et al.
Published: (2025)

Leakage Safe Graph Features for Interpretable Fraud Detection in Temporal Transaction Networks
by: Khaleghpour, Hamideh, et al.
Published: (2026)

SecONNds: Secure Outsourced Neural Network Inference on ImageNet
by: Balla, Shashank
Published: (2025)

Provable Privacy Attacks on Trained Shallow Neural Networks
by: Smorodinsky, Guy, et al.
Published: (2024)

Stealing Training Graphs from Graph Neural Networks
by: Lin, Minhua, et al.
Published: (2024)

Privacy-Preserving 3-Layer Neural Network Training
by: Chiang, John
Published: (2023)

SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges
by: Panzade, Prajwal, et al.
Published: (2022)

PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks
by: Wang, Fuyi, et al.
Published: (2025)

Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
by: Zhou, Yvonne, et al.
Published: (2026)

Mitigating Error Amplification in Fast Adversarial Training
by: Zhao, Mengnan, et al.
Published: (2026)

CBNN: 3-Party Secure Framework for Customized Binary Neural Networks Inference
by: Dong, Benchang, et al.
Published: (2024)

PEEL the Layers and Find Yourself: Revisiting Inference-time Data Leakage for Residual Neural Networks
by: Arif, Huzaifa, et al.
Published: (2025)

Tempo: Confidentiality Preservation in Cloud-Based Neural Network Training
by: Xu, Rongwu, et al.
Published: (2024)

Location Leakage in Federated Signal Maps
by: Bakopoulou, Evita, et al.
Published: (2021)

Enhancing Network Security: A Hybrid Approach for Detection and Mitigation of Distributed Denial-of-Service Attacks Using Machine Learning
by: Shohan, Nizo Jaman, et al.
Published: (2025)

Encryption-Friendly LLM Architecture
by: Rho, Donghwan, et al.
Published: (2024)

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

Data Leakage in Automotive Perception: Practitioners' Insights
by: Babu, Md Abu Ahammed, et al.
Published: (2026)

HEART: A High-Efficiency Adaptive Real-Time Telemonitoring Framework for Secure Electrocardiogram Signal Transmission Using Chaotic Encryption
by: Yuksel, Beyazıt Bestami
Published: (2026)