Copertina

CBNN: 3-Party Secure Framework for Customized Binary Neural Networks Inference

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Dong, Benchang, Chen, Zhili, Chen, Xin, Wei, Shiwen, Fu, Jie, Li, Huifa
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Cryptography and Security
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
di: Cheng, Tong, et al.
Pubblicazione: (2025)

Safeguarding Graph Neural Networks against Topology Inference Attacks
di: Fu, Jie, et al.
Pubblicazione: (2025)

FOBNN: Fast Oblivious Inference via Binarized Neural Networks
di: Chen, Xin, et al.
Pubblicazione: (2024)

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

ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
di: Ling, Xinpeng, et al.
Pubblicazione: (2023)

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

FedFDP: Fairness-Aware Federated Learning with Differential Privacy
di: Ling, Xinpeng, et al.
Pubblicazione: (2024)

SecONNds: Secure Outsourced Neural Network Inference on ImageNet
di: Balla, Shashank
Pubblicazione: (2025)

Attack on a PUF-based Secure Binary Neural Network
di: Basak, Bijeet, et al.
Pubblicazione: (2025)

Secure Transformer Inference Protocol
di: Yuan, Mu, et al.
Pubblicazione: (2023)

Multi-Party Private Set Intersection: A Circuit-Based Protocol with Jaccard Similarity for Secure and Efficient Anomaly Detection in Network Traffic
di: Su, Jiuheng, et al.
Pubblicazione: (2024)

An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs
di: Thomas, Rahul, et al.
Pubblicazione: (2025)

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

Privacy-Preserving Verifiable Neural Network Inference Service
di: Riasi, Arman, et al.
Pubblicazione: (2024)

Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions
di: Awasthi, Anant Prakash, et al.
Pubblicazione: (2024)

Differentially Private Federated Learning: A Systematic Review
di: Fu, Jie, et al.
Pubblicazione: (2024)

ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
di: Cheng, Zhan, et al.
Pubblicazione: (2025)

N-Parties Private Structure and Parameter Learning for Sum-Product Networks
di: Heilmann, Xenia, et al.
Pubblicazione: (2025)

Hard-Label Cryptanalytic Extraction of Neural Network Models
di: Chen, Yi, et al.
Pubblicazione: (2024)

Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Accelerators
di: Banerjee, Sarbartha, et al.
Pubblicazione: (2024)

Panther: A Cost-Effective Privacy-Preserving Framework for GNN Training and Inference Services in Cloud Environments
di: Chen, Congcong, et al.
Pubblicazione: (2025)

SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks
di: Tavallaie, Omid, et al.
Pubblicazione: (2024)

Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
di: Diaa, Abdulrahman, et al.
Pubblicazione: (2023)

Certified Defense on the Fairness of Graph Neural Networks
di: Dong, Yushun, et al.
Pubblicazione: (2023)

Spin: An Efficient Secure Computation Framework with GPU Acceleration
di: Jiang, Wuxuan, et al.
Pubblicazione: (2024)

Graph Neural Network Explanations are Fragile
di: Li, Jiate, et al.
Pubblicazione: (2024)

Functional Encryption in Secure Neural Network Training: Data Leakage and Practical Mitigations
di: Ioniţă, Alexandru, et al.
Pubblicazione: (2025)

BADTV: Unveiling Backdoor Threats in Third-Party Task Vectors
di: Hsu, Chia-Yi, et al.
Pubblicazione: (2025)

On Training a Neural Network to Explain Binaries
di: Interrante-Grant, Alexander, et al.
Pubblicazione: (2024)

Secure Aggregation is Not Private Against Membership Inference Attacks
di: Ngo, Khac-Hoang, et al.
Pubblicazione: (2024)

NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping
di: Ju, Jae Hyung, et al.
Pubblicazione: (2023)

Dash: Accelerating Distributed Private Convolutional Neural Network Inference with Arithmetic Garbled Circuits
di: Sander, Jonas, et al.
Pubblicazione: (2023)

How does Graph Structure Modulate Membership-Inference Risk for Graph Neural Networks?
di: Khosla, Megha
Pubblicazione: (2026)

Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling
di: Wang, Shaowei, et al.
Pubblicazione: (2024)

Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks
di: Dong, Xuewen, et al.
Pubblicazione: (2025)

Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesis
di: Chen, Kai, et al.
Pubblicazione: (2025)

Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees
di: Quijano, Andrew, et al.
Pubblicazione: (2025)

Ditto: Quantization-aware Secure Inference of Transformers upon MPC
di: Wu, Haoqi, et al.
Pubblicazione: (2024)

Efficient Skip Connections Realization for Secure Inference on Encrypted Data
di: Drucker, Nir, et al.
Pubblicazione: (2023)

Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations
di: Li, Jiate, et al.
Pubblicazione: (2025)