Search Results for author: Yifeng Zheng

Found 8 papers, 2 papers with code

Vertical Federated Learning: Taxonomies, Threats, and Prospects

no code implementations3 Feb 2023 Qun Li, Chandra Thapa, Lawrence Ong, Yifeng Zheng, Hua Ma, Seyit A. Camtepe, Anmin Fu, Yansong Gao

In a number of practical scenarios, VFL is more relevant than HFL as different companies (e. g., bank and retailer) hold different features (e. g., credit history and shopping history) for the same set of customers.

Federated Learning

BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label

1 code implementation1 Jul 2022 Shengshan Hu, Ziqi Zhou, Yechao Zhang, Leo Yu Zhang, Yifeng Zheng, Yuanyuan HE, Hai Jin

In this paper, we propose BadHash, the first generative-based imperceptible backdoor attack against deep hashing, which can effectively generate invisible and input-specific poisoned images with clean label.

Backdoor Attack Contrastive Learning +3

SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service

1 code implementation16 Feb 2022 Songlei Wang, Yifeng Zheng, Xiaohua Jia

With the proliferation of cloud computing, it is increasingly popular to deploy the services of complex and resource-intensive model training and inference in the cloud due to its prominent benefits.

Cloud Computing Privacy Preserving

Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

no code implementations4 Feb 2022 Yifeng Zheng, Shangqi Lai, Yi Liu, Xingliang Yuan, Xun Yi, Cong Wang

In this paper, we present a system design which offers efficient protection of individual model updates throughout the learning procedure, allowing clients to only provide obscured model updates while a cloud server can still perform the aggregation.

Federated Learning

NTD: Non-Transferability Enabled Backdoor Detection

no code implementations22 Nov 2021 Yinshan Li, Hua Ma, Zhi Zhang, Yansong Gao, Alsharif Abuadbba, Anmin Fu, Yifeng Zheng, Said F. Al-Sarawi, Derek Abbott

A backdoor deep learning (DL) model behaves normally upon clean inputs but misbehaves upon trigger inputs as the backdoor attacker desires, posing severe consequences to DL model deployments.

Face Recognition Traffic Sign Recognition

RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things

no code implementations9 May 2021 Huming Qiu, Hua Ma, Zhi Zhang, Yifeng Zheng, Anmin Fu, Pan Zhou, Yansong Gao, Derek Abbott, Said F. Al-Sarawi

To this end, a 1-bit quantized DNN model or deep binary neural network maximizes the memory efficiency, where each parameter in a BNN model has only 1-bit.


Decamouflage: A Framework to Detect Image-Scaling Attacks on Convolutional Neural Networks

no code implementations8 Oct 2020 Bedeuro Kim, Alsharif Abuadbba, Yansong Gao, Yifeng Zheng, Muhammad Ejaz Ahmed, Hyoungshick Kim, Surya Nepal

To corroborate the efficiency of Decamouflage, we have also measured its run-time overhead on a personal PC with an i5 CPU and found that Decamouflage can detect image-scaling attacks in milliseconds.


Evaluation of Federated Learning in Phishing Email Detection

no code implementations27 Jul 2020 Chandra Thapa, Jun Wen Tang, Alsharif Abuadbba, Yansong Gao, Seyit Camtepe, Surya Nepal, Mahathir Almashor, Yifeng Zheng

For a fixed total email dataset, the global RNN based model suffers by a 1. 8% accuracy drop when increasing organizational counts from 2 to 10.

Distributed Computing Federated Learning +2

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