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Jin Li - IEEE Xplore Author Profile
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Jin Li

Publications
104
Citations
8,171
Publications by Year
20092025
Co-Authors:
Show All Co-Authors (261)
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Jin Li

Affiliation

Institute of Artificial Intelligence, Guangzhou University, China

Publication Topics

Biography

Jin Li (Senior Member, IEEE) received the B.S. degree, in 2002, M.S. degree, in 2004, both in mathematics, from Southwest University and Sun Yat-sen University respectively, and the Ph.D. degree in information security from Sun Yat-sen University, in 2007. He is currently a Professor and an Executive Dean of the Institute of Artificial Intelligence and Blockchain, Guangzhou University. His research interests include the design of secure protocols in cloud computing and cryptographic protocols. He has published more than 100 papers in international conferences and journals, including IEEE INFOCOM, IEEE Transactions on Information Forensics and Security, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Computers, an...
Publications
104
Citations
8,171
Publications by Year
20092025
Co-Authors:
Show All Co-Authors (261)
Author's Published Works

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Differential Private Data Stream Analytics in the Local and Shuffle Models

Shaowei Wang;Jin Li;Yun Peng;Kongyang Chen;Wei Yang;Hui Jiang;Jin Li

IEEE Transactions on Mobile Computing
Year: 2025 | Early Access Article |
We study online data analytics with differential privacy (DP) in decentralized settings. Specifically, online data analytics with local DP protection is widely adopted in real-world applications. Despite numerous endeavors in this field, significant gaps in utility and functionality remain when compared to its offline counterpart. We present an optimal, streamable mechanism: ExSub, for local DP sp...Show More

Differential Private Data Stream Analytics in the Local and Shuffle Models

Shaowei Wang;Jin Li;Yun Peng;Kongyang Chen;Wei Yang;Hui Jiang;Jin Li

Year: 2025 | Early Access Article |

RMR: A Relative Membership Risk Measure for Machine Learning Models

Li Bai;Haibo Hu;Qingqing Ye;Jianliang Xu;Jin Li;Chengfang Fang;Jie Shi

IEEE Transactions on Dependable and Secure Computing
Year: 2025 | Early Access Article |
Privacy leakage poses a significant threat when machine learning foundation models trained on private data are released. One such threat is membership inference attacks (MIA), which determine whether a specific example was included in a model's training set. This paper shifts focus from developing new MIA algorithms to measuring a model's risk under MIA. We introduce a novel metric, Relative Membe...Show More

RMR: A Relative Membership Risk Measure for Machine Learning Models

Li Bai;Haibo Hu;Qingqing Ye;Jianliang Xu;Jin Li;Chengfang Fang;Jie Shi

Year: 2025 | Early Access Article |

VAE-Based Membership Cleanser Against Membership Inference Attacks

Li Hu;Hongyang Yan;Yun Peng;Haibo Hu;Shaowei Wang;Jin Li

IEEE Transactions on Dependable and Secure Computing
Year: 2025 | Volume: 22, Issue: 2 | Journal Article |
Membership inference attacks (MIAs) compromise the privacy of training data through interrogating a victim machine learning model and inferring whether or not a query sample is in the training data. Existing defenses against MIAs include preprocessing the training data of the model, modifying loss functions, and perturbing the inference output. However, all these mechanisms have to change either t...Show More

VAE-Based Membership Cleanser Against Membership Inference Attacks

Li Hu;Hongyang Yan;Yun Peng;Haibo Hu;Shaowei Wang;Jin Li

Year: 2025 | Volume: 22, Issue: 2 | Journal Article |

Differentially Private Numerical Vector Analyses in the Local and Shuffle Model

Shaowei Wang;Shiyu Yu;Xiaojun Ren;Jin Li;Yuntong Li;Wei Yang;Hongyang Yan;Jin Li

IEEE Transactions on Dependable and Secure Computing
Year: 2025 | Volume: 22, Issue: 1 | Journal Article |
Numerical vector aggregation plays a crucial role in privacy-sensitive applications, such as distributed gradient estimation in federated learning and statistical analysis of key-value data. In the context of local differential privacy, this study provides a tight minimax error bound of $O(\frac{ds}{n\epsilon ^{2}})$O(dsnε2), where $d$d represents the dimension of the numerical vector and $s$s den...Show More

Differentially Private Numerical Vector Analyses in the Local and Shuffle Model

Shaowei Wang;Shiyu Yu;Xiaojun Ren;Jin Li;Yuntong Li;Wei Yang;Hongyang Yan;Jin Li

Year: 2025 | Volume: 22, Issue: 1 | Journal Article |

Sweeper: Breaking the Validity-Latency Tradeoff in Asynchronous Common Subset

Guoyu Yang;Chang Chen;Qi Chen;Jianan Jiang;Jin Li;Debiao He

IEEE Transactions on Information Forensics and Security
Year: 2024 | Volume: 19 | Journal Article |
Cited by: Papers (1)
Asynchronous common subset (ACS) is an essential building block for Byzantine fault-tolerance and multi-party computation. The classic ACS framework is due to Ben-Or, Kemler, and Rabin (BKR), consisting of ${n}$ reliable broadcast (RBC) instances and ${n}$ asynchronous binary agreement (ABA) instances (where ${n}$ is the total number of replicas). Despite recent progresses of practical BKR-A...Show More

Sweeper: Breaking the Validity-Latency Tradeoff in Asynchronous Common Subset

Guoyu Yang;Chang Chen;Qi Chen;Jianan Jiang;Jin Li;Debiao He

Year: 2024 | Volume: 19 | Journal Article |

Building a Lightweight Trusted Execution Environment for Arm GPUs

Chenxu Wang;Yunjie Deng;Zhenyu Ning;Kevin Leach;Jin Li;Shoumeng Yan;Zhengyu He;Jiannong Cao;Fengwei Zhang

IEEE Transactions on Dependable and Secure Computing
Year: 2024 | Volume: 21, Issue: 4 | Journal Article |
Cited by: Papers (2)
A wide range of Arm endpoints leverage integrated and discrete GPUs to accelerate computation. However, Arm GPU security has not been explored by the community. Existing work has used Trusted Execution Environments (TEEs) to address GPU security concerns on Intel-based platforms, but there are numerous architectural differences that lead to novel technical challenges in deploying TEEs for Arm GPUs...Show More

Building a Lightweight Trusted Execution Environment for Arm GPUs

Chenxu Wang;Yunjie Deng;Zhenyu Ning;Kevin Leach;Jin Li;Shoumeng Yan;Zhengyu He;Jiannong Cao;Fengwei Zhang

Year: 2024 | Volume: 21, Issue: 4 | Journal Article |

A Secure and Flexible Blockchain-Based Offline Payment Protocol

Wanqing Jie;Wangjie Qiu;Arthur Sandor Voundi Koe;Jianhong Li;Yin Wang;Yaqi Wu;Jin Li;Zhiming Zheng

IEEE Transactions on Computers
Year: 2024 | Volume: 73, Issue: 2 | Journal Article |
Cited by: Papers (12)
Off-chain transactions seek to address the low on-chain scalability and enable blockchain-based payments over unreliable on-chain networks. The key problem with existing works is that they fail to balance security and flexibility in their designs. These studies would have been more useful if they could provide a sense of security without compromising their flexibility. We hypothesize that two offl...Show More

A Secure and Flexible Blockchain-Based Offline Payment Protocol

Wanqing Jie;Wangjie Qiu;Arthur Sandor Voundi Koe;Jianhong Li;Yin Wang;Yaqi Wu;Jin Li;Zhiming Zheng

Year: 2024 | Volume: 73, Issue: 2 | Journal Article |

Cross-Shaped Adversarial Patch Attack

Yu Ran;Weijia Wang;Mingjie Li;Lin-Cheng Li;Yuan-Gen Wang;Jin Li

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 4 | Journal Article |
Cited by: Papers (11)
Recent studies have shown that deep learning-based classifiers are vulnerable to malicious inputs, i.e., adversarial examples. A practical solution is to construct a perceptible but localized perturbation called patch, making the well-trained models misclassified. However, most existing patch-based adversarial attacks focus on designing patches with localized rectangles, squares, or grids, ignorin...Show More

Cross-Shaped Adversarial Patch Attack

Yu Ran;Weijia Wang;Mingjie Li;Lin-Cheng Li;Yuan-Gen Wang;Jin Li

Year: 2024 | Volume: 34, Issue: 4 | Journal Article |

DTrust: Toward Dynamic Trust Levels Assessment in Time-Varying Online Social Networks

Jie Wen;Nan Jiang;Jin Li;Ximeng Liu;Honglong Chen;Yanzhi Ren;Zhaohui Yuan;Ziang Tu

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
Year: 2023 | Conference Paper |
The social trust assessment can spur extensive applications such as social recommendations, shopping, financial investment strategies, etc, but remain a challenging problem having limited exploration. Such explorations mainly limit their studies to static network topology or simplified dynamic networks, toward the social trust relationship prediction. In contrast, in this paper, we explore the soc...Show More

DTrust: Toward Dynamic Trust Levels Assessment in Time-Varying Online Social Networks

Jie Wen;Nan Jiang;Jin Li;Ximeng Liu;Honglong Chen;Yanzhi Ren;Zhaohui Yuan;Ziang Tu

Year: 2023 | Conference Paper |

Multifactor Incentive Mechanism for Federated Learning in IoT: A Stackelberg Game Approach

Yuling Chen;Hui Zhou;Tao Li;Jin Li;Huiyu Zhou

IEEE Internet of Things Journal
Year: 2023 | Volume: 10, Issue: 24 | Journal Article |
Cited by: Papers (13)
In the era of the Internet of Things (IoT), remote sensors and endpoint appliances generate vast amounts of data. Decentralized and collaborative learning builds on these IoT data to enable classification and recognition tasks by inviting multiple data owners. Federated learning (FL), as a popular collaborative learning framework, can significantly improve the performance of models without collect...Show More

Multifactor Incentive Mechanism for Federated Learning in IoT: A Stackelberg Game Approach

Yuling Chen;Hui Zhou;Tao Li;Jin Li;Huiyu Zhou

Year: 2023 | Volume: 10, Issue: 24 | Journal Article |

Differential Aggregation against General Colluding Attackers

Rong Du;Qingqing Ye;Yue Fu;Haibo Hu;Jin Li;Chengfang Fang;Jie Shi

2023 IEEE 39th International Conference on Data Engineering (ICDE)
Year: 2023 | Conference Paper |
Cited by: Papers (7)
Local Differential Privacy (LDP) is now widely adopted in large-scale systems to collect and analyze sensitive data while preserving users’ privacy. However, almost all LDP protocols rely on a semi-trust model where users are curious-but-honest, which rarely holds in real-world scenarios. Recent works [6], [11], [62] show poor estimation accuracy of many LDP protocols under malicious threat models...Show More

Differential Aggregation against General Colluding Attackers

Rong Du;Qingqing Ye;Yue Fu;Haibo Hu;Jin Li;Chengfang Fang;Jie Shi

Year: 2023 | Conference Paper |

3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning

Haoyang Li;Qingqing Ye;Haibo Hu;Jin Li;Leixia Wang;Chengfang Fang;Jie Shi

2023 IEEE Symposium on Security and Privacy (SP)
Year: 2023 | Conference Paper |
Cited by: Papers (22)
Federated Learning (FL), the de-facto distributed machine learning paradigm that locally trains datasets at individual devices, is vulnerable to backdoor model poisoning attacks. By compromising or impersonating those devices, an attacker can upload crafted malicious model updates to manipulate the global model with backdoor behavior upon attacker-specified triggers. However, existing backdoor att...Show More

3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning

Haoyang Li;Qingqing Ye;Haibo Hu;Jin Li;Leixia Wang;Chengfang Fang;Jie Shi

Year: 2023 | Conference Paper |

Collaborative Sampling for Partial Multi-Dimensional Value Collection Under Local Differential Privacy

Qiuyu Qian;Qingqing Ye;Haibo Hu;Kai Huang;Tom Tak-Lam Chan;Jin Li

IEEE Transactions on Information Forensics and Security
Year: 2023 | Volume: 18 | Journal Article |
Cited by: Papers (4)
In big data era, companies and organizations are keen to collect data from users and analyse their behaviour patterns to make decisions or predictions for profits. However, it undermines users’ privacy because the collected data can be quite sensitive and easy to leak. To address privacy problems, local differential privacy (LDP) has been proposed for untrusted data collectors to obtain statistica...Show More

Collaborative Sampling for Partial Multi-Dimensional Value Collection Under Local Differential Privacy

Qiuyu Qian;Qingqing Ye;Haibo Hu;Kai Huang;Tom Tak-Lam Chan;Jin Li

Year: 2023 | Volume: 18 | Journal Article |

Semi-Fragile Reversible Watermarking for 3D Models Using Spherical Crown Volume Division

Fei Peng;Tongxin Liao;Min Long;Jin Li;Wensheng Zhang;Yicong Zhou

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2023 | Volume: 33, Issue: 11 | Journal Article |
Cited by: Papers (3)
Aiming at the large distortion and low tampering localization accuracy of the existing semi-fragile reversible watermarking for 3D mesh models, a novel semi-fragile reversible watermarking for 3D models using spherical crown volume division is proposed. The crown volume of a sphere is divided to reduce the embedding distortion. The possible geometric and topological transformations are separately ...Show More

Semi-Fragile Reversible Watermarking for 3D Models Using Spherical Crown Volume Division

Fei Peng;Tongxin Liao;Min Long;Jin Li;Wensheng Zhang;Yicong Zhou

Year: 2023 | Volume: 33, Issue: 11 | Journal Article |

Sequential Order-Aware Coding-Based Robust Subspace Clustering for Human Action Recognition in Untrimmed Videos

Zhili Zhou;Chun Ding;Jin Li;Eman Mohammadi;Guangcan Liu;Yimin Yang;Q. M. Jonathan Wu

IEEE Transactions on Image Processing
Year: 2023 | Volume: 32 | Journal Article |
Cited by: Papers (14)
Human action recognition (HAR) is one of most important tasks in video analysis. Since video clips distributed on networks are usually untrimmed, it is required to accurately segment a given untrimmed video into a set of action segments for HAR. As an unsupervised temporal segmentation technology, subspace clustering learns the codes from each video to construct an affinity graph, and then cuts th...Show More

Sequential Order-Aware Coding-Based Robust Subspace Clustering for Human Action Recognition in Untrimmed Videos

Zhili Zhou;Chun Ding;Jin Li;Eman Mohammadi;Guangcan Liu;Yimin Yang;Q. M. Jonathan Wu

Year: 2023 | Volume: 32 | Journal Article |

Secret-to-Image Reversible Transformation for Generative Steganography

Zhili Zhou;Yuecheng Su;Jin Li;Keping Yu;Q. M. Jonathan Wu;Zhangjie Fu;Yunqing Shi

IEEE Transactions on Dependable and Secure Computing
Year: 2023 | Volume: 20, Issue: 5 | Journal Article |
Cited by: Papers (48)
Recently, generative steganography that transforms secret information to a generated image has been a promising technique to resist steganalysis detection. However, due to the inefficiency and irreversibility of the secret-to-image transformation, it is hard to find a good trade-off between the information hiding capacity and extraction accuracy. To address this issue, we propose a secret-to-image...Show More

Secret-to-Image Reversible Transformation for Generative Steganography

Zhili Zhou;Yuecheng Su;Jin Li;Keping Yu;Q. M. Jonathan Wu;Zhangjie Fu;Yunqing Shi

Year: 2023 | Volume: 20, Issue: 5 | Journal Article |

GAN-Siamese Network for Cross-Domain Vehicle Re-Identification in Intelligent Transport Systems

Zhili Zhou;Yujiang Li;Jin Li;Keping Yu;Guang Kou;Meimin Wang;Brij Bhooshan Gupta

IEEE Transactions on Network Science and Engineering
Year: 2023 | Volume: 10, Issue: 5 | Journal Article |
Cited by: Papers (47)
The vehicle re-identification (Re-ID) has become one of most important techniques for tracking vehicles in intelligent transport system. Vehicle Re-ID aims at matching identical vehicle images captured by different surveillance cameras. Recent vehicle Re-ID approaches explored deep learning-based features or distance metric learning methods for vehicle matching. However, most of the existing appro...Show More

GAN-Siamese Network for Cross-Domain Vehicle Re-Identification in Intelligent Transport Systems

Zhili Zhou;Yujiang Li;Jin Li;Keping Yu;Guang Kou;Meimin Wang;Brij Bhooshan Gupta

Year: 2023 | Volume: 10, Issue: 5 | Journal Article |

Defending Against Membership Inference Attacks With High Utility by GAN

Li Hu;Jin Li;Guanbiao Lin;Shiyu Peng;Zhenxin Zhang;Yingying Zhang;Changyu Dong

IEEE Transactions on Dependable and Secure Computing
Year: 2023 | Volume: 20, Issue: 3 | Journal Article |
Cited by: Papers (20)
The success of machine learning (ML) depends on the availability of large-scale datasets. However, recent studies have shown that models trained on such datasets are vulnerable to privacy attacks, among which membership inference attack (MIA) brings serious privacy risk. MIA allows an adversary to infer whether a sample belongs to the training dataset of the target model or not. Though a variety o...Show More

Defending Against Membership Inference Attacks With High Utility by GAN

Li Hu;Jin Li;Guanbiao Lin;Shiyu Peng;Zhenxin Zhang;Yingying Zhang;Changyu Dong

Year: 2023 | Volume: 20, Issue: 3 | Journal Article |

GATrust: A Multi-Aspect Graph Attention Network Model for Trust Assessment in OSNs

Nan Jiang;Jie Wen;Jin Li;Ximeng Liu;Di Jin

IEEE Transactions on Knowledge and Data Engineering
Year: 2023 | Volume: 35, Issue: 6 | Journal Article |
Cited by: Papers (48)
Social trust assessment that characterizes a pairwise trustworthiness relationship can spur diversified applications. Extensive efforts have been put in exploration, but mainly focusing on applying graph convolutional network to establish a social trust evaluation model, overlooking user feature factors related to context-aware information on social trust prediction. In this article, we aim to des...Show More

GATrust: A Multi-Aspect Graph Attention Network Model for Trust Assessment in OSNs

Nan Jiang;Jie Wen;Jin Li;Ximeng Liu;Di Jin

Year: 2023 | Volume: 35, Issue: 6 | Journal Article |

Attacking Deep Reinforcement Learning With Decoupled Adversarial Policy

Kanghua Mo;Weixuan Tang;Jin Li;Xu Yuan

IEEE Transactions on Dependable and Secure Computing
Year: 2023 | Volume: 20, Issue: 1 | Journal Article |
Cited by: Papers (52)
While Deep Reinforcement Learning (DRL) has achieved outstanding performance in extensive applications, exploiting its vulnerability with adversarial attacks is essential towards building robust DRL systems. In this work, we aim to propose a novel Decoupled Adversarial Policy (DAP) for attacking the DRL mechanism, whereas the adversarial agent can decompose the adversarial policy into two separate...Show More

Attacking Deep Reinforcement Learning With Decoupled Adversarial Policy

Kanghua Mo;Weixuan Tang;Jin Li;Xu Yuan

Year: 2023 | Volume: 20, Issue: 1 | Journal Article |

Secure Aggregation is Insecure: Category Inference Attack on Federated Learning

Jiqiang Gao;Boyu Hou;Xiaojie Guo;Zheli Liu;Ying Zhang;Kai Chen;Jin Li

IEEE Transactions on Dependable and Secure Computing
Year: 2023 | Volume: 20, Issue: 1 | Journal Article |
Cited by: Papers (24)
Federated learning allows a large number of resource-constrained clients to train a globally-shared model together without sharing local data. These clients usually have only a few classes (categories) of data for training, where the data distribution is non-iid (not independent identically distributed). In this article, we put forward the concept of category privacy for the first time to indicate...Show More

Secure Aggregation is Insecure: Category Inference Attack on Federated Learning

Jiqiang Gao;Boyu Hou;Xiaojie Guo;Zheli Liu;Ying Zhang;Kai Chen;Jin Li

Year: 2023 | Volume: 20, Issue: 1 | Journal Article |

Privacy-Preserving Multi-Granular Federated Neural Architecture Search – A General Framework

Zijie Pan;Li Hu;Weixuan Tang;Jin Li;Yi He;Zheli Liu

IEEE Transactions on Knowledge and Data Engineering
Year: 2023 | Volume: 35, Issue: 3 | Journal Article |
Cited by: Papers (12)
Jointly learning from multiple datasets can help building versatile intelligent systems yet may give rise to serious concerns of data privacy and model selection. Specifically, on the one hand, these datasets can be distributed at various local clients, who may not be willing or do not ought to share data with each other. On the other hand, it is unrealistic to choose a model architecture that can...Show More

Privacy-Preserving Multi-Granular Federated Neural Architecture Search – A General Framework

Zijie Pan;Li Hu;Weixuan Tang;Jin Li;Yi He;Zheli Liu

Year: 2023 | Volume: 35, Issue: 3 | Journal Article |

Blockchain-Based Secure Key Management for Mobile Edge Computing

Jiaxing Li;Jigang Wu;Long Chen;Jin Li;Siew-Kei Lam

IEEE Transactions on Mobile Computing
Year: 2023 | Volume: 22, Issue: 1 | Journal Article |
Cited by: Papers (31)
Mobile edge computing (MEC) is a promising edge technology to provide high bandwidth and low latency shared services and resources to mobile users. However, the MEC infrastructure raises major security concerns when the shared resources involve sensitive and private data of users. This paper proposes a novel blockchain-based key management scheme for MEC that is essential for ensuring secure group...Show More

Blockchain-Based Secure Key Management for Mobile Edge Computing

Jiaxing Li;Jigang Wu;Long Chen;Jin Li;Siew-Kei Lam

Year: 2023 | Volume: 22, Issue: 1 | Journal Article |

Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge

Weixuan Tang;Bin Li;Mauro Barni;Jin Li;Jiwu Huang

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2022 | Volume: 32, Issue: 6 | Journal Article |
Cited by: Papers (19)
Although significant progress has been achieved recently in automatic learning of steganographic cost, the existing methods designed for spatial images cannot be directly applied to JPEG images which are more common media in daily life. The difficulties of migration are mainly caused by the characteristics of the $8\times 8$ DCT mode structure. To address the issue, in this paper we extend an ex...Show More

Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge

Weixuan Tang;Bin Li;Mauro Barni;Jin Li;Jiwu Huang

Year: 2022 | Volume: 32, Issue: 6 | Journal Article |

A Novel Rank Learning Based No-Reference Image Quality Assessment Method

Fu-Zhao Ou;Yuan-Gen Wang;Jin Li;Guopu Zhu;Sam Kwong

IEEE Transactions on Multimedia
Year: 2022 | Volume: 24 | Journal Article |
Cited by: Papers (24)
Recently, applying deep learning to no-reference image quality assessment (NR-IQA) has received significant attention. Especially in the last five years, an increasing interest has been drawn to the studies of rank learning since it can help mitigate the problem of small IQA datasets. However, on one hand, existing rank learning is not suitable for the authentically distorted images due to the lac...Show More

A Novel Rank Learning Based No-Reference Image Quality Assessment Method

Fu-Zhao Ou;Yuan-Gen Wang;Jin Li;Guopu Zhu;Sam Kwong

Year: 2022 | Volume: 24 | Journal Article |

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