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MD-SONIC: Maliciously-Secure Outsourcing Neural Network Inference with Reduced Online Communication
Yansong Zhang;Xiaojun Chen;Ye Dong;Qinghui Zhang;Rui Hou;Qiang Liu;Xudong Chen
IEEE Transactions on Information Forensics and Security
Year: 2025 | Early Access Article |
With the widespread deployment of Deep-Learningas-a-Service, secure multi-party computation-based outsourcing neural network (NN) inference has garnered significant attention for its high-security guarantee. Nevertheless, under the dishonest-majority setting with malicious adversaries, prior secure inference works are still costly in terms of communication and run-time. Additionally, existing outs...Show More
Take Attention Inside: Neighbor Pair Graph Contrastive Learning
Bisheng Tang;Xiaojun Chen;Shaopu Wang;Yuexin Xuan;Zhendong Zhao
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Graph Contrastive Learning(GCL) is a fundamental pretraining research method in Graph Neural Networks (GNNs), which puts rich graph-level insights into the graph data to augment the data representation. However, since the existing GCLs generally regard the intra-layer node as negative samples, they cannot cope with the diverse coupled neighbor relationships, which can be pre-trained with the combi...Show More
Lightweight Secure Aggregation for Personalized Federated Learning with Backdoor Resistance
Tingyu Fan;Xiaojun Chen;Ye Dong;Xudong Chen;Yuexin Xuan;Weizhan Jing
2024 Annual Computer Security Applications Conference (ACSAC)
Year: 2024 | Conference Paper |
Existing federated learning (FL) systems are highly vulnerable in terms of security and privacy due to their distributed architecture, facing poisoning attacks and inference attacks from adversaries. Some prior works have combined poisoning defenses with cryptographic tools: Secure Multi-Party Computation, Zero-Knowledge Proof, and Homomorphic Encryption to propose robust secure aggregation method...Show More
DualCOS: Query-Efficient Data-Free Model Stealing with Dual Clone Networks and Optimal Samples
2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
Although data-free model stealing attacks are free from reliance on real data, they suffer from limitations, including low accuracy and high query budgets, which restrict their practical feasibility. In this paper, we propose a novel data-free model stealing framework called DualCOS. As a whole, DualCOS is divided into two stages: interactive training and semi-supervised boosting. To optimize the ...Show More
STMS: An Out-Of-Distribution Model Stealing Method Based on Causality
Yunfei Yang;Xiaojun Chen;Zhendong Zhao;Yuexin Xuan;Bisheng Tang;Xiaoying Li
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Machine learning, particularly deep learning, is extensively applied in various real-life scenarios. However, recent research has highlighted the severe infringement of privacy and intellectual property caused by model stealing attacks. Therefore, more researchers are dedicated to studying the principles and methods of such attacks to promote the security development of artificial intelligence. Mo...Show More
Comet: Communication-Efficient Batch Secure Three-Party Neural Network Inference with Client-Aiding
ICC 2024 - IEEE International Conference on Communications
Year: 2024 | Conference Paper |
Secure neural network inference enables server (model provider) and client to perform neural network inference without leaking their private inputs. Existing SOTA three-party computation (3PC) inference works emerge challenges on two fronts: i) GPU-accelerated CryptGPU (S&P'21) and P-FALCON (USENIX Security'22) face challenges related to high communication overhead. ii) communication-efficient Met...Show More
Roger: A Round Optimized GPU-Friendly Secure Inference Framework
Xudong Chen;Xiaojun Chen;Ye Dong;Weizhan Jing;Tingyu Fan;Qinghui Zhang
ICC 2024 - IEEE International Conference on Communications
Year: 2024 | Conference Paper |
Secure neural network inference provides a promising solution to preserve the privacy of Deep Learning as a Service (DLaaS), but its substantial communication and computation overhead remain challenging. Recent works such as GForce [1] and Piranha [2] have introduced GPU-friendly secure inference protocols with improved computation efficiency, yet these approaches are either limited to supporting ...Show More
FlexBNN: Fast Private Binary Neural Network Inference With Flexible Bit-Width
Advancements in deep learning enable neural network (NN) inference to be a service, but service providers and clients want to keep their inputs secret for privacy protection. Private Inference is the task of evaluating NN without leaking private inputs. Existing secure multiparty computation (MPC)-based solutions mainly focus on fixed bit-width methodology, such as 32 and 64 bits. Binary Neural Ne...Show More
NEWSFARM: A Large-Scale Chinese Corpus of Long News Summarization
Shunan Zang;Chuang Zhang;Xiaojun Liu;Xiaojun Chen;Peng Zhang;Jie Liu
2022 26th International Conference on Pattern Recognition (ICPR)
Year: 2022 | Conference Paper |
Recently, the field of natural language processing (NLP) has grown rapidly, driven by massive datasets. At the same time, the need for automatic summarization systems has been rapidly increasing as the amount of textual information on the web and in large data centers became intractable for human readers. However, the lack of large-scale and high-quality Chinese datasets remain a critical bottlene...Show More
ACTSS: Input Detection Defense against Backdoor Attacks via Activation Subset Scanning
Yuexin Xuan;Xiaojun Chen;Zhendong Zhao;Yangyang Ding;Jianming Lv
2022 International Joint Conference on Neural Networks (IJCNN)
Year: 2022 | Conference Paper |
Deep neural networks are vulnerable to backdoor attacks where adversaries inject the trigger into partial training data to manipulate the trained model misclassification. In addition, the poisoned model behaves normally on clean inputs, and the malicious behavior only occurs when the secret trigger is present, making backdoor attacks hard to be detected. Most existing input detection methods lever...Show More
Rethinking the Feature Iteration Process of Graph Convolution Networks
2022 International Joint Conference on Neural Networks (IJCNN)
Year: 2022 | Conference Paper |
Node classification is a fundamental research problem in graph neural networks(GNNs), which uses node's feature and label to capture node embedding in a low dimension. The existing graph node classification approaches mainly focus on GNNs from global and local perspectives. The relevant research is relatively insufficient for the micro perspective, which refers to the feature itself. In this paper...Show More
KAFNN: A Knowledge Augmentation Framework to Graph Neural Networks
2022 International Joint Conference on Neural Networks (IJCNN)
Year: 2022 | Conference Paper |
The semi-supervised node classification task is a basic problem in graph neural networks(GNNs). GNNs have shown their superiority in graph datasets over traditional neural networks such as Multilayer Perceptron. However, due to the limitation of Weisfeiler-Lehman, the existing GNNs will discard some prior knowledge, which is hard to be coped with, such as Dropout skill, etc. In this paper, we prop...Show More
DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints
Zhendong Zhao;Xiaojun Chen;Yuexin Xuan;Ye Dong;Dakui Wang;Kaitai Liang
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (38)
Backdoor attack is a type of serious security threat to deep learning models. An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor. The backdoored models behave normally on clean images, yet can be activated and output incorrect prediction if the input is stamped with a specific trigger pattern. Most existing backdoor...Show More
Noise Suppression with Label Graph in Distantly Supervised Relation Extraction
2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Year: 2022 | Conference Paper |
Distantly supervised relation extraction suffers from the influence of noise data. Some works solved this issue with relation-aware attention based on multi-instance learning to reduce the weights of noise data in sentence bag, which achieved remarkable results. However, the relationships of label-label and label-sentence in semantic space that can enhance label representation effectively was igno...Show More
A Differential Privacy Collaborative Deep Learning Algorithm in Pervasive Edge Computing Environment
2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
With the development of 5G technology and intelligent terminals, the future direction of the Industrial Internet of Things (IIoT) evolution is Pervasive Edge Computing (PEC). In the pervasive edge computing environment, intelligent terminals can perform calculations and data processing. By migrating part of the original cloud computing model's calculations to intelligent terminals, the intelligent...Show More
A Differential Privacy Collaborative Deep Learning Algorithm in Pervasive Edge Computing Environment
Year: 2021 | Conference Paper |
SoMem: A Self-optimizing Memory Network for Distributed Person Re-identification
2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)
Year: 2020 | Conference Paper |
Person Re-Identification (Re-ID) aims to match the persons contained in surveillance videos, and is usually run on powerful servers in a supervised mode. However, centralized processing of massive video from thousands of cameras in a city is very costly and causes serious problems of privacy protection. Moreover, the labeling of numerous data for supervised training is also infeasible in this scen...Show More
Improving Abstractive Summarization with Iterative Representation
2020 International Joint Conference on Neural Networks (IJCNN)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
In the neural abstractive summarization field, comprehensive document representation and summary embellishment are two major challenges. To tackle the above problems, we propose an Iterative Abstractive Summarization (IAS) model through iterating the document and summary representation. Specifically, (1) we design a selective gated strategy to constantly update the input representation in the enco...Show More
Improving Abstractive Text Summarization with History Aggregation
2020 International Joint Conference on Neural Networks (IJCNN)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
Recent neural sequence to sequence models have provided feasible solutions for abstractive summarization. However, such models are still hard to tackle long text dependency in the summarization task. A high-quality summarization system usually depends on strong encoder which can refine important information from long input texts so that the decoder can generate salient summaries from the encoder's...Show More
The matching efficiency of content-based publish and subscribe model(pub/sub model) can be more optimized since users on Internet have different needs for data and popular data is always changing. In this paper, a brand new matching model - Quasi-Optimal Binary Search Tree (QOBST) which is adaptive was proposed. Based on the idea of optimal binary search tree and avoiding the complex construction ...Show More
Abstractive Text Summarization with Multi-Head Attention
Jinpeng Li;Chuang Zhang;Xiaojun Chen;Yanan Cao;Pengcheng Liao;Peng Zhang
2019 International Joint Conference on Neural Networks (IJCNN)
Year: 2019 | Conference Paper |
Cited by: Papers (13)
In this paper, we present a novel sequence-to-sequence architecture with multi-head attention for automatic summarization of long text. Summaries generated by previous abstractive methods have the problems of duplicate and missing original information commonly. To address these problems, we propose a multi-head attention summarization (MHAS) model, which uses multi-head attention mechanism to lear...Show More
A More Efficient Private Set Intersection Protocol Based on Random OT and Balance Hash
ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Private set intersection (PSI) is a specific application problem in the field of secure multi-party computation. It allows the participants to compute set intersection collaboratively without learning any additional information about the sets. It is a building block for many real-world privacy-related applications. The state-of-the-art PSI protocol is mainly based on Random Oblivious Transfer (ROT...Show More
An On-the-Fly Scheduling Strategy for Distributed Stream Processing Platform
Wen'an Wang;Chuang Zhang;Xiaojun Chen;Zhao Li;Hong Ding;Xin Wen
2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Distributed stream processing can accomplish real-time processing of continuous streaming big data to obtain valuable information with high velocity. To maintain continuously stable and efficient running of stream applications, however, continuous online scheduling operations are required in the context of highly dynamic data stream. For this reason, this paper proposes the on-the-fly scheduling s...Show More
In order to improve the efficiency of real-time data transmission between different Internet Data Centers (IDCs) by content, this paper proposes a content-based real-time message-oriented middleware for cloud environment (Cloud-MOM). Nodes of Cloud-MOM can be deployed in IDCs in different regions. The messages from each IDC can be routed and transmitted through nodes efficiently. In another way, f...Show More
Efficient and Private Set Intersection of Human Genomes
2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2018 | Conference Paper |
Cited by: Papers (5)
With the development of human genomes sequencing technology, the biological and medical research has been greatly accelerated and a wide range of health-related applications and services become more and more ubiquitous and affordable. However, the digitized genomes sequence raises serious privacy issues since a genome contains individual's extremely sensitive information. In this paper, we mainly ...Show More
Improve Word Mover's Distance with Part-of-Speech Tagging
2018 24th International Conference on Pattern Recognition (ICPR)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
Word Mover's Distance (WMD) is a document distance metric with free parameter, intelligible interpretation and unprecedented accuracy on document classification. WMD is on the basis of word embedding and largely focuses on semantic relationships rather than syntactic relationships, which would bring some limitations on measuring document distance. To enhance the impact of syntactic information, we...Show More
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