11 citations · 38 across the 18 of their papers we have counts for
19 papers
Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables
Yifeng Zheng, Shuangqing Xu, Songlei Wang +2
Vertical federated learning (VFL) has recently emerged as an appealing distributed paradigm empowering multi-party collaboration for training high-quality models over vertically pa…
Vertical Federated Learning: Taxonomies, Threats, and Prospects
Qun Li, Chandra Thapa, Lawrence Ong +5
Federated learning (FL) is the most popular distributed machine learning technique. FL allows machine-learning models to be trained without acquiring raw data to a single point for…
Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints
Minghui Li, Wei Wan, Jianrong Lu +5
Federated learning is a newly emerging distributed learning framework that facilitates the collaborative training of a shared global model among distributed participants with their…
OblivGM: Oblivious Attributed Subgraph Matching as a Cloud Service
Songlei Wang, Yifeng Zheng, Xiaohua Jia +2
In recent years there has been growing popularity of leveraging cloud computing for storing and querying attributed graphs, which have been widely used to model complex structured…
SecSkyline: Fast Privacy-Preserving Skyline Queries over Encrypted Cloud Databases
Yifeng Zheng, Weibo Wang, Songlei Wang +3
The well-known benefits of cloud computing have spurred the popularity of database service outsourcing, where one can resort to the cloud to conveniently store and query databases.…
MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning
Hua Ma, Qun Li, Yifeng Zheng +5
Federated Learning (FL), a distributed machine learning paradigm, has been adapted to mitigate privacy concerns for customers. Despite their appeal, there are various inference att…
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label
Shengshan Hu, Ziqi Zhou, Yechao Zhang +4
Due to its powerful feature learning capability and high efficiency, deep hashing has achieved great success in large-scale image retrieval. Meanwhile, extensive works have demonst…
Privacy-Preserving Analytics on Decentralized Social Graphs: The Case of Eigendecomposition
Songlei Wang, Yifeng Zheng, Xiaohua Jia +1
Analytics over social graphs allows to extract valuable knowledge and insights for many fields like community detection, fraud detection, and interest mining. In practice, decentra…
Towards Privacy-Preserving and Verifiable Federated Matrix Factorization
Xicheng Wan, Yifeng Zheng, Qun Li +3
Recent years have witnessed the rapid growth of federated learning (FL), an emerging privacy-aware machine learning paradigm that allows collaborative learning over isolated datase…
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization
Yifeng Zheng, Shangqi Lai, Yi Liu +3
Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features th…
SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service
Songlei Wang, Yifeng Zheng, Xiaohua Jia
Graphs are widely used to model the complex relationships among entities. As a powerful tool for graph analytics, graph neural networks (GNNs) have recently gained wide attention d…
NTD: Non-Transferability Enabled Backdoor Detection
Yinshan Li, Hua Ma, Zhi Zhang +6
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 d…
Optimizing Secure Decision Tree Inference Outsourcing
Yifeng Zheng, Cong Wang, Ruochen Wang +2
Outsourcing decision tree inference services to the cloud is highly beneficial, yet raises critical privacy concerns on the proprietary decision tree of the model provider and the…
SEDML: Securely and Efficiently Harnessing Distributed Knowledge in Machine Learning
Yansong Gao, Qun Li, Yifeng Zheng +3
Training high-performing deep learning models require a rich amount of data which is usually distributed among multiple data sources in practice. Simply centralizing these multi-so…
RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things
Huming Qiu, Hua Ma, Zhi Zhang +6
Though deep neural network models exhibit outstanding performance for various applications, their large model size and extensive floating-point operations render deployment on mobi…
Can Differential Privacy Practically Protect Collaborative Deep Learning Inference for the Internet of Things?
Jihyeon Ryu, Yifeng Zheng, Yansong Gao +6
Collaborative inference has recently emerged as an attractive framework for applying deep learning to Internet of Things (IoT) applications by splitting a DNN model into several su…
Decamouflage: A Framework to Detect Image-Scaling Attacks on Convolutional Neural Networks
Bedeuro Kim, Alsharif Abuadbba, Yansong Gao +4
As an essential processing step in computer vision applications, image resizing or scaling, more specifically downsampling, has to be applied before feeding a normally large image…
Cloud-based Privacy-Preserving Collaborative Consumption for Sharing Economy
Lingjuan Lyu, Sid Chi-Kin Chau, Nan Wang +1
Cloud computing has been a dominant paradigm for a variety of information processing platforms, particularly for enabling various popular applications of sharing economy. However,…
Evaluation of Federated Learning in Phishing Email Detection
Chandra Thapa, Jun Wen Tang, Alsharif Abuadbba +5
The use of Artificial Intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which opens it up to a myriad of privacy, trust, and l…