Publications (63)
Research on the Security of Blockchain Data: A Survey
Liehuang Zhu, Baokun Zheng, Meng Shen +5
With the more and more extensive application of blockchain, blockchain security has been widely concerned by the society and deeply studied by scholars. Moreover, the security of b…
Privacy-Preserving Semantic Communication over Wiretap Channels with Learnable Differential Privacy
Weixuan Chen, Qianqian Yang, Shuo Shao +3
While semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information, it also raises critical privacy concerns. Many existing secure SemC…
SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +1
Deep learning (DL) enabled semantic communications leverage DL to train encoders and decoders (codecs) to extract and recover semantic information. However, most semantic training…
Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +2
Machine unlearning is a fundamental mechanism that enforces the right to be forgotten. Existing unlearning studies that rely on label manipulation or task-gradient reversal often d…
GeoIB: Geometry-Aware Information Bottleneck via Statistical-Manifold Compression
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +1
Information Bottleneck (IB) is widely used, but in deep learning, it is usually implemented through tractable surrogates, such as variational bounds or neural mutual information (M…
A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks
Haomiao Yang, Kunlan Xiang, Mengyu Ge +3
The Large Language Models (LLMs) are poised to offer efficient and intelligent services for future mobile communication networks, owing to their exceptional capabilities in languag…
Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning
Xuhan Zuo, Minghao Wang, Tianqing Zhu +4
The development of Large Language Models (LLMs) faces a significant challenge: the exhausting of publicly available fresh data. This is because training a LLM needs a large demandi…
Too Expensive to Attack: A Joint Defense Framework to Mitigate Distributed Attacks for the Internet of Things Grid
Jianhua Li, Ximeng Liu, Jiong Jin +1
The distributed denial of service (DDoS) attack is detrimental to businesses and individuals as we are heavily relying on the Internet. Due to remarkable profits, crackers favor DD…
Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
Yichen Li, Haozhao Wang, Wenchao Xu +9
Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-st…
Too Expensive to Attack: Enlarge the Attack Expense through Joint Defense at the Edge
Jianhua Li, Ximeng Liu, Jiong JIn +1
The distributed denial of service (DDoS) attack is detrimental to businesses and individuals as people are heavily relying on the Internet. Due to remarkable profits, crackers favo…
Invisibility Cloak: Disappearance under Human Pose Estimation via Backdoor Attacks
Minxing Zhang, Wenshu Fan, Wenbo Jiang +3
Despite being significant in autonomous systems, Human Pose Estimation (HPE)'s potential risks to adversarial attacks have not received comparable attention with image classificati…
SMS: Self-supervised Model Seeding for Verification of Machine Unlearning
Weiqi Wang, Chenhan Zhang, Zhiyi Tian +1
Many machine unlearning methods have been proposed recently to uphold users' right to be forgotten. However, offering users verification of their data removal post-unlearning is an…
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times
Yao Fu, Yipeng Zhou, Di Wu +3
In spite that Federated Learning (FL) is well known for its privacy protection when training machine learning models among distributed clients collaboratively, recent studies have…
Revealing Multimodal Causality with Large Language Models
Jin Li, Shoujin Wang, Qi Zhang +5
Uncovering cause-and-effect mechanisms from data is fundamental to scientific progress. While large language models (LLMs) show promise for enhancing causal discovery (CD) from uns…
Optimal Repair Strategy Against Advanced Persistent Threats Under Time-Varying Networks
Zixuan Wang, Jiliang Li, Yuntao Wang +3
Advanced persistent threat (APT) is a kind of stealthy, sophisticated, and long-term cyberattack that has brought severe financial losses and critical infrastructure damages. Exist…
Towards Blockchain-Assisted Privacy-Aware Data Sharing For Edge Intelligence: A Smart Healthcare Perspective
Youyang Qu, Lichuan Ma, Wenjie Ye +4
The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of smart healthcare networks (SHNs). To enhance the precision of di…
Complex Network Theoretical Analysis on Information Dissemination over Vehicular Networks
Jingjing Wang, Chunxiao Jiang, Longxiang Gao +3
How to enhance the communication efficiency and quality on vehicular networks is one critical important issue. While with the larger and larger scale of vehicular networks in dense…
Towards Fair Large Language Model-based Recommender Systems without Costly Retraining
Jin Li, Huilin Gu, Shoujin Wang +5
Large Language Models (LLMs) have revolutionized Recommender Systems (RS) through advanced generative user modeling. However, LLM-based RS (LLM-RS) often inadvertently perpetuates…
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment
Jianguo Chen, Kenli Li, Zhuo Tang +4
With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both acade…
Understanding the Robustness of Graph Neural Networks against Adversarial Attacks
Tao Wu, Canyixing Cui, Xingping Xian +4
Recent studies have shown that graph neural networks (GNNs) are vulnerable to adversarial attacks, posing significant challenges to their deployment in safety-critical scenarios. T…
DDFAD: Dataset Distillation Framework for Audio Data
Wenbo Jiang, Rui Zhang, Hongwei Li +3
Deep neural networks (DNNs) have achieved significant success in numerous applications. The remarkable performance of DNNs is largely attributed to the availability of massive, hig…
Peripheral-free Device Pairing by Randomly Switching Power
Zhijian Shao, Jian Weng, Yue Zhang +5
The popularity of Internet-of-Things (IoT) comes with security concerns. Attacks against wireless communication venues of IoT (e.g., Man-in-the-Middle attacks) have grown at an ala…
Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems
Binbin Jin, Enhong Chen, Hongke Zhao +4
In the area of community question answering (CQA), answer selection and answer ranking are two tasks which are applied to help users quickly access valuable answers. Existing solut…
A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation
Yakun Chen, Kaize Shi, Zhangkai Wu +5
Spatiotemporal data analysis is pivotal across various domains, such as transportation, meteorology, and healthcare. The data collected in real-world scenarios are often incomplete…
New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook
Meng Yang, Tianqing Zhu, Chi Liu +3
Thanks to the explosive growth of data and the development of computational resources, it is possible to build pre-trained models that can achieve outstanding performance on variou…
A Framework to Prevent Biometric Data Leakage in the Immersive Technologies Domain
Keshav Sood, Iynkaran Natgunanathan, Uthayasanker Thayasivam +3
Doubtlessly, the immersive technologies have potential to ease people's life and uplift economy, however the obvious data privacy risks cannot be ignored. For example, a participan…
HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning
Wenqiang Ruan, Xin Lin, Ruisheng Zhou +3
Multi-party computation (MPC) based machine learning, referred to as multi-party learning (MPL), has become an important technology for utilizing data from multiple parties with pr…
Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System
Jiwei Guan, Lei Pan, Chen Wang +3
There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become avai…
Variational Co-embedding Learning for Attributed Network Clustering
Shuiqiao Yang, Sunny Verma, Borui Cai +4
Recent works for attributed network clustering utilize graph convolution to obtain node embeddings and simultaneously perform clustering assignments on the embedding space. It is e…
Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacy
Yipeng Zhou, Xuezheng Liu, Yao Fu +3
Federated learning (FL) empowers distributed clients to collaboratively train a shared machine learning model through exchanging parameter information. Despite the fact that FL can…
Ellipsoid Control: A White-list Jailbreak Defense via Benign Latent Modeling
Luoyu Chen, Weiqi Wang, Zhiyi Tian +3
Representation engineering (RepE) defenses have shown strong robustness against jailbreak attacks on large language models (LLMs). However, these methods fundamentally rely on blac…
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning
Jiahao Tan, Yipeng Zhou, Gang Liu +2
The federated learning (FL) paradigm emerges to preserve data privacy during model training by only exposing clients' model parameters rather than original data. One of the biggest…
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation
Tian Liu, Zhiwei Ling, Jun Xia +3
As a promising distributed machine learning paradigm, Federated Learning (FL) trains a central model with decentralized data without compromising user privacy, which has made it wi…
TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning
Weiqi Wang, Zhiyi Tian, An Liu +1
With the increasing prevalence of Web-based platforms handling vast amounts of user data, machine unlearning has emerged as a crucial mechanism to uphold users' right to be forgott…
Diffusion Models for Reinforcement Learning: Foundations, Taxonomy, and Development
Changfu Xu, Jianxiong Guo, Yuzhu Liang +7
Diffusion Models (DMs), as a leading class of generative models, offer key advantages for reinforcement learning (RL), including multi-modal expressiveness, stable training, and tr…
Machine Learning Empowered Intelligent Data Center Networking: A Survey
Bo Li, Ting Wang, Peng Yang +3
To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high comp…
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
Xuhan Zuo, Minghao Wang, Tianqing Zhu +3
With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imp…
BioZKFHE: Scalable Encrypted Biometric Identification via Verifiable Homomorphic Similarity Evaluation
Rundong Xin, Taotao Wang, Xiaoxiao Wu +3
Large-scale biometric identification in outsourced settings requires two properties simultaneously: biometric templates and queries must remain protected during computation, and th…
High-frequency Matters: An Overwriting Attack and defense for Image-processing Neural Network Watermarking
Huajie Chen, Tianqing Zhu, Chi Liu +2
In recent years, there has been significant advancement in the field of model watermarking techniques. However, the protection of image-processing neural networks remains a challen…
Steering Beyond the Support: Adversarial Training on Unsupervised Jailbroken Activation Simulation
Luoyu Chen, Weiqi Wang, Zhiyi Tian +5
Jailbreak prompts can trigger harmful completions on aligned LLMs, In accordance, safety steering has been proposed: test-time activation interventions that steer jailbreak activat…
BlindU: Blind Machine Unlearning without Revealing Erasing Data
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +1
Machine unlearning enables data holders to remove the contribution of their specified samples from trained models to protect their privacy. However, it is paradoxical that most unl…
EVE: Efficient Verification of Data Erasure through Customized Perturbation in Approximate Unlearning
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +2
Verifying whether the machine unlearning process has been properly executed is critical but remains underexplored. Some existing approaches propose unlearning verification methods…
Incentivizing Users of Data Centers Participate in The Demand Response Programs via Time-Varying Monetary Rewards
Yong Zhan, Du Xu, Hongfang Yu +1
Demand response is widely employed by today's data centers to reduce energy consumption in response to the increasing of electricity cost. To incentivize users of data centers part…
CRFU: Compressive Representation Forgetting Against Privacy Leakage on Machine Unlearning
Weiqi Wang, Chenhan Zhang, Zhiyi Tian +2
Machine unlearning allows data owners to erase the impact of their specified data from trained models. Unfortunately, recent studies have shown that adversaries can recover the era…
Joint Beamforming and Illumination Pattern Design for Beam-Hopping LEO Satellite Communications
Jing Wang, Chenhao Qi, Shui Yu +1
Since hybrid beamforming (HBF) can approach the performance of fully-digital beamforming (FDBF) with much lower hardware complexity, we investigate the HBF design for beam-hopping…
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
Yuyang Zhou, Guang Cheng, Zongyao Chen +1
Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid proliferation of malware attacks. However, r…
Machine Unlearning: A Comprehensive Survey
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +1
As the right to be forgotten has been legislated worldwide, many studies attempt to design unlearning mechanisms to protect users' privacy when they want to leave machine learning…
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis
Xiaozheng Xie, Jianwei Niu, Xuefeng Liu +3
Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area. To addres…
An Overview of Attacks and Defences on Intelligent Connected Vehicles
Mahdi Dibaei, Xi Zheng, Kun Jiang +9
Cyber security is one of the most significant challenges in connected vehicular systems and connected vehicles are prone to different cybersecurity attacks that endanger passengers…
Social-Aware Clustered Federated Learning with Customized Privacy Preservation
Yuntao Wang, Zhou Su, Yanghe Pan +3
A key feature of federated learning (FL) is to preserve the data privacy of end users. However, there still exist potential privacy leakage in exchanging gradients under FL. As a r…
Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations
Jin Li, Shoujin Wang, Qi Zhang +2
Incomplete scenario is a prevalent, practical, yet challenging setting in Multimodal Recommendations (MMRec), where some item modalities are missing due to various factors. Recentl…
ZK-AMS: Credibly Anonymous Admission for Web 3.0 Platforms via Recursive Proof Aggregation
Zibin Lin, Taotao Wang, Shengli Zhang +3
Web 3.0 platforms need an onboarding mechanism that can admit real users at scale without forcing them to reveal identity documents or pay one on-chain verification cost per user.…
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security
Youyang Qu, Ming Liu, Tianqing Zhu +3
Federated Learning (FL) offers a promising paradigm for training Large Language Models (LLMs) in a decentralized manner while preserving data privacy and minimizing communication o…
Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration
Xuhan Zuo, Minghao Wang, Tianqing Zhu +2
Large language models (LLMs) have transformed the way computers understand and process human language, but using them effectively across different organizations remains still diffi…
Crowdsourcing-based Multi-Device Communication Cooperation for Mobile High-Quality Video Enhancement
Xiaotong Wu, Lianyong Qi, Xiaolong Xu +3
The widespread use of mobile devices propels the development of new-fashioned video applications like 3D (3-Dimensional) stereo video and mobile cloud game via web or App, exerting…
Data Sharing, Privacy and Security Considerations in the Energy Sector: A Review from Technical Landscape to Regulatory Specifications
Shiliang Zhang, Sabita Maharjan, Lee Andrew Bygrave +1
Decarbonization, decentralization and digitalization are the three key elements driving the twin energy transition. The energy system is evolving to a more data driven ecosystem, l…
Inference Attacks: A Taxonomy, Survey, and Promising Directions
Feng Wu, Lei Cui, Shaowen Yao +1
The prosperity of machine learning has also brought people's concerns about data privacy. Among them, inference attacks can implement privacy breaches in various MLaaS scenarios an…
"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems
Beining Wu, Jun Huang, Shui Yu
The development of next-generation networking systems has inherently shifted from throughput-based paradigms towards intelligent, information-aware designs that emphasize the quali…
Can Steering Wheel Detect Your Driving Fatigue?
Jianchao Lu, Xi Zheng, Tianyi Zhang +5
Automated Driving System (ADS) has attracted increasing attention from both industrial and academic communities due to its potential for increasing the safety, mobility and efficie…
Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models
Jianxing Liao, Junyan Xu, Yatao Sun +6
Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. W…
From Human Interfaces to Agent Interfaces: Rethinking Software Design in the Age of AI-Native Systems
Shaolin Wang, Yi Mei, Haoyang Che +3
Software systems have traditionally been designed for human interaction, emphasizing graphical user interfaces, usability, and cognitive alignment with end users. However, recent a…
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?
Chenhan Zhang, Weiqi Wang, Zhiyi Tian +4
Although recent advancements in end-to-end learning-based link prediction (LP) methods have shown remarkable capabilities, the significance of traditional similarity-based LP metho…
Stealthy Targeted Backdoor Attacks against Image Captioning
Wenshu Fan, Hongwei Li, Wenbo Jiang +3
In recent years, there has been an explosive growth in multimodal learning. Image captioning, a classical multimodal task, has demonstrated promising applications and attracted ext…