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学者姓名：公备
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Abstract ：
In the era of data-driven artificial intelligence, the widespread deployment of IoT devices has amplified concerns around privacy and data security. Federated learning (FL) offers a promising solution by enabling local model training without exposing raw data, effectively mitigating privacy risks. However, the inherent heterogeneity of IoT data leads to significant disparities in data distributions across different clients, negatively impacting the global model's performance. Furthermore, conventional fixed differential privacy mechanisms lack the adaptability needed to dynamically adjust to the evolving requirements of different training phases, limiting their effectiveness in privacy-preserving federated learning. To address these challenges, we propose a federated learning framework called AWE-DPFL, which integrates adaptive weight fusion and dynamic privacy budget adjustment mechanisms. AWE-DPFL employs a dynamic privacy budget adjustment strategy to allocate privacy budgets based on the variance in client model updates, thereby improving model performance while ensuring robust privacy protection. Additionally, the adaptive weight fusion mechanism assigns different weights to each client's model, taking into account data heterogeneity and quality, which leads to an enhanced global model that better reflects individual client contributions. Moreover, AWE-DPFL incorporates meta-learning alongside differential privacy techniques during local model training, resulting in an effective balance between data privacy and model performance. This approach not only improves model adaptability and generalization across diverse data distributions but also ensures that privacy requirements are met throughout the training process. Experimental evaluations demonstrate that AWE-DPFL significantly outperforms existing approaches on the MNIST, FashionMNIST, HAR, and Edge-IIoTset datasets, showcasing its effectiveness as a federated learning solution for real-world IoT applications.
Keyword ：
Weight fusion Weight fusion Federated learning Federated learning Internet of things Internet of things Privacy budget adjustment Privacy budget adjustment Differential privacy Differential privacy
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| GB/T 7714 | Zheng, Guiping , Gong, Bei , Guo, Chong et al. AWE-DPFL: Adaptive weighting and dynamic privacy budget federated learning for heterogeneous data in IoT [J]. | COMPUTERS & ELECTRICAL ENGINEERING , 2025 , 123 . |
| MLA | Zheng, Guiping et al. "AWE-DPFL: Adaptive weighting and dynamic privacy budget federated learning for heterogeneous data in IoT" . | COMPUTERS & ELECTRICAL ENGINEERING 123 (2025) . |
| APA | Zheng, Guiping , Gong, Bei , Guo, Chong , Peng, Tianqi , Gong, Mowei . AWE-DPFL: Adaptive weighting and dynamic privacy budget federated learning for heterogeneous data in IoT . | COMPUTERS & ELECTRICAL ENGINEERING , 2025 , 123 . |
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Abstract ：
Dynamic symmetric searchable encryption (DSSE) allows clients to perform keyword searches and updates on encrypted databases outsourced to cloud servers. Ensuring forward privacy is a crucial security property for DSSE schemes to protect data privacy. However, existing forward-private DSSE schemes face significant limitations: they either rely on an honest-but-curious server, assuming it always returns correct search results without providing verification functionality, or they lack support for fine-grained attribute-based searches and access control. As a result, these schemes cannot be directly applied to attribute-based databases. In this paper, we propose the first verifiable forward-private DSSE scheme suitable for attribute-based databases. Specifically, we construct a secure index based on attribute elements to realize fine-grained searches on attribute-value type databases while ensuring the forward privacy of the scheme. We also design a novel verification tag using symmetric homomorphic encryption to verify the correctness of search results. In addition, our scheme achieves access control functionality to ensure that different users can only access authorized files. Experimental evaluations show that our scheme has advantage in the update, search and verification processes. And the security analysis proves our scheme is secure.
Keyword ：
Attribute-value type databases Attribute-value type databases Forward privacy Forward privacy Verification Verification Dynamic symmetric searchable encryption Dynamic symmetric searchable encryption
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| GB/T 7714 | Peng, Tianqi , Gong, Bei , Sun, Pengxuan . Toward privacy-preserving verifiable DSSE for attribute-based cloud computing system [J]. | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (2) . |
| MLA | Peng, Tianqi et al. "Toward privacy-preserving verifiable DSSE for attribute-based cloud computing system" . | JOURNAL OF SUPERCOMPUTING 81 . 2 (2025) . |
| APA | Peng, Tianqi , Gong, Bei , Sun, Pengxuan . Toward privacy-preserving verifiable DSSE for attribute-based cloud computing system . | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (2) . |
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Abstract ：
In the cloud-assisted industrial Internet of Things (IIoT), since the cloud server is not always trusted, the leakage of data privacy becomes a critical problem. Dynamic symmetric searchable encryption (DSSE) allows for the secure retrieval of outsourced data stored on cloud servers while ensuring data privacy. Forward privacy and backward privacy are necessary security requirements for DSSE. However, most existing schemes either trade the server's large storage overhead for forward privacy or trade efficiency/overhead for weak backward privacy. These schemes cannot fully meet the security requirements of cloud-assisted IIoT systems. We propose a fast and firmly secure SSE scheme called Veruna to address these limitations. To this end, we design a new state chain structure, which can not only ensure forward privacy with less storage overhead of the server but also achieve strong backward privacy with only a few cryptographic operations in the server. Security analysis proves that our scheme possesses forward privacy and Type-II backward privacy. Compared with many state-of-the-art schemes, our scheme has an advantage in search and update performance. The high efficiency and robust security make Veruna an ideal scheme for deployment in cloud-assisted IIoT systems.
Keyword ：
state chain structure state chain structure symmetric searchable encryption symmetric searchable encryption forward and backward privacy forward and backward privacy cloud-assisted IIoT cloud-assisted IIoT
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| GB/T 7714 | Peng, Tianqi , Gong, Bei , Tu, Shanshan et al. Forward and Backward Private Searchable Encryption for Cloud-Assisted Industrial IoT [J]. | SENSORS , 2024 , 24 (23) . |
| MLA | Peng, Tianqi et al. "Forward and Backward Private Searchable Encryption for Cloud-Assisted Industrial IoT" . | SENSORS 24 . 23 (2024) . |
| APA | Peng, Tianqi , Gong, Bei , Tu, Shanshan , Namoun, Abdallah , Alshmrany, Sami , Waqas, Muhammad et al. Forward and Backward Private Searchable Encryption for Cloud-Assisted Industrial IoT . | SENSORS , 2024 , 24 (23) . |
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Abstract ：
The Internet of Things (IoT) is a heterogeneous network composed of numerous dynamically connected devices. While it brings convenience, the IoT also faces serious challenges in data security. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptography method that supports fine-grained access control, offering a solution to the IoT's security issues. However, existing CP-ABE schemes are inefficient and unsuitable for IoT devices with limited computing resources. To address this problem, this paper proposes an efficient pairing-free CP-ABE scheme for the IoT. The scheme is based on lightweight elliptic curve scalar multiplication and supports multi-authority and verifiable outsourced decryption. The proposed scheme satisfies indistinguishability against chosen-plaintext attacks (CPA) under the elliptic curve decisional Diffie-Hellman (ECDDH) problem. Performance analysis shows that our proposed scheme is more efficient and better suited to the IoT environment compared to existing schemes.
Keyword ：
ciphertext-policy attribute-based encryption ciphertext-policy attribute-based encryption pairing-free pairing-free access control access control Internet of Things Internet of Things
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| GB/T 7714 | Guo, Chong , Gong, Bei , Waqas, Muhammad et al. An Efficient Pairing-Free Ciphertext-Policy Attribute-Based Encryption Scheme for Internet of Things [J]. | SENSORS , 2024 , 24 (21) . |
| MLA | Guo, Chong et al. "An Efficient Pairing-Free Ciphertext-Policy Attribute-Based Encryption Scheme for Internet of Things" . | SENSORS 24 . 21 (2024) . |
| APA | Guo, Chong , Gong, Bei , Waqas, Muhammad , Alasmary, Hisham , Tu, Shanshan , Chen, Sheng . An Efficient Pairing-Free Ciphertext-Policy Attribute-Based Encryption Scheme for Internet of Things . | SENSORS , 2024 , 24 (21) . |
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Abstract ：
Internet of things (IoT) development tends to reduce the reliance on centralized servers. The zero-trust distributed system combined with blockchain technology has become a hot topic in IoT research. However, distribution data storage services and different blockchain protocols make network interoperability and cross-platform more complex. Relay chain is a promising cross-chain technology that solves the complexity and compatibility issues associated with blockchain cross-chain transactions by utilizing relay blockchains as cross-chain connectors. Yet relay chain cross-chain transactions need to collect asset information and implement asset transactions via two-way peg. Due to the release of user transaction information, there is the issue of privacy leakage. In this article, we propose a cross-chain privacy protection protocol based on the Groth16 zero-knowledge proof algorithm and coin-mixing technology, which changes the authentication mechanism and uses a combination of generating functions to map virtual external addresses in transactions. It allows fast cross-chain anonymous transactions while hiding the genuine user's address. The experiment shows that, in a zero-trust IoT context, our scheme can effectively protect user privacy information, accomplish controlled transaction traceability operations, and guarantee cross-chain transaction security.
Keyword ：
relay chain relay chain privacy protection privacy protection Zero trust Zero trust vritual address vritual address groth16 groth16
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| GB/T 7714 | Yang, Yinghong , Bai, Fenhua , Yu, Zhuo et al. An Anonymous and Super visory Cross-chain Privacy Protection Protocol for Zero-trust IoT Application [J]. | ACM TRANSACTIONS ON SENSOR NETWORKS , 2024 , 20 (2) . |
| MLA | Yang, Yinghong et al. "An Anonymous and Super visory Cross-chain Privacy Protection Protocol for Zero-trust IoT Application" . | ACM TRANSACTIONS ON SENSOR NETWORKS 20 . 2 (2024) . |
| APA | Yang, Yinghong , Bai, Fenhua , Yu, Zhuo , Shen, Tao , Liu, Yingli , Gong, Bei . An Anonymous and Super visory Cross-chain Privacy Protection Protocol for Zero-trust IoT Application . | ACM TRANSACTIONS ON SENSOR NETWORKS , 2024 , 20 (2) . |
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Abstract ：
To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm ' s complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency.
Keyword ：
Blockchain Blockchain secret sharing secret sharing Byzantine fault tolerance Byzantine fault tolerance zero trust zero trust consensus algorithm consensus algorithm
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| GB/T 7714 | Tian, Siben , Bai, Fenhua , Shen, Tao et al. yVSSB-Raft: A Secure and Efficient Zero Trust Consensus Algorithm for Blockchain [J]. | ACM TRANSACTIONS ON SENSOR NETWORKS , 2024 , 20 (2) . |
| MLA | Tian, Siben et al. "yVSSB-Raft: A Secure and Efficient Zero Trust Consensus Algorithm for Blockchain" . | ACM TRANSACTIONS ON SENSOR NETWORKS 20 . 2 (2024) . |
| APA | Tian, Siben , Bai, Fenhua , Shen, Tao , Zhang, Chi , Gong, Bei . yVSSB-Raft: A Secure and Efficient Zero Trust Consensus Algorithm for Blockchain . | ACM TRANSACTIONS ON SENSOR NETWORKS , 2024 , 20 (2) . |
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Abstract ：
With the continuous development of mobile devices, mobile applications bring a lot of convenience to people's lives. The abuse of mobile device permissions is prone to the risk of privacy leakage. The existing detection technology can detect the inconsistency between the declared authority and the actual use authority. But using the third-party privacy policy as the analysis basis for SDK permissions will result in a large set of extracted declaration permissions, which will lead to identifying risky applications as normal applications during consistency comparison. The prevailing approach involves utilizing models based on TextCNN to extract information from privacy policies. However, the training of TextCNN relies on large-scale annotated datasets, leading to high costs. This paper uses BERT as the word vector extraction model to obtain private phrases from the privacy policy. And then we use cosine similarity to automatically filter permission phrase samples, reducing the workload of manual labeling. On the other hand, existing methods do not support the analysis of Chinese privacy policies. In order to solve the problem of consistency judgment between Chinese privacy policy and permission usage, we implement a BERT-based Android privacy policy and permission usage consistency analysis engine. The engine first uses static analysis to obtain the permission list of Android applications, and then combines the BERT model to achieve consistency analysis. After functional and speed testing, we found that the engine can successfully run the consistency analysis function of Chinese declaration permissions and usage permissions, and it is better than the existing detection methods.
Keyword ：
Privacy policy Privacy policy Mobile applications Mobile applications Policy conflicts Policy conflicts Text extraction Text extraction Android security Android security
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| GB/T 7714 | Tu, Tengfei , Zhang, Hua , Gong, Bei et al. Intelligent analysis of android application privacy policy and permission consistency [J]. | ARTIFICIAL INTELLIGENCE REVIEW , 2024 , 57 (7) . |
| MLA | Tu, Tengfei et al. "Intelligent analysis of android application privacy policy and permission consistency" . | ARTIFICIAL INTELLIGENCE REVIEW 57 . 7 (2024) . |
| APA | Tu, Tengfei , Zhang, Hua , Gong, Bei , Du, Daizhong , Wen, Qiaoyan . Intelligent analysis of android application privacy policy and permission consistency . | ARTIFICIAL INTELLIGENCE REVIEW , 2024 , 57 (7) . |
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Abstract ：
Green edge computing aims to reasonably allocate computing resources on the premise of ensuring the reliability of information services. The computing power gap between terminal and edge server makes the traditional encryption algorithm waste too much energy when dealing with massive redundant data. How to improve encryption efficiency and reduce the computing consumption of massive data terminal equipment on the premise of ensuring data security is one of the goals of green edge computing. We proposed a data compression and encryption scheme based on compression sensing, which greatly reduces the computing consumption of computing limited data terminals; At the same time, the hyper chaotic system is used to further encrypt the data by Arnold transform, bitwise XOR and data random scrambling. In order to solve the problem that compressed sensing can not accurately recover data, we designed a nonlinear encryption scheme based on Chinese Remainder Theorem as a supplement. The simulation results show that the proposed data compression and encryption method is effective and reliable, which has high security performance, compression ability for text data and images, and high recovery ability when the compression ratio is more than 0.5.
Keyword ：
Chinese remainder theorem Chinese remainder theorem Green edge computing Green edge computing Compression encryption Compression encryption Compressed sensing Compressed sensing
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| GB/T 7714 | Liu, Jianli , Zhang, Yu , Gong, Bei . A data compression and encryption method for green edge computing [J]. | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS , 2023 , 26 (5) : 3341-3359 . |
| MLA | Liu, Jianli et al. "A data compression and encryption method for green edge computing" . | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 26 . 5 (2023) : 3341-3359 . |
| APA | Liu, Jianli , Zhang, Yu , Gong, Bei . A data compression and encryption method for green edge computing . | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS , 2023 , 26 (5) , 3341-3359 . |
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Abstract ：
Differential privacy provides strong privacy preservation guarantee in information sharing. As social network analysis has been enjoying many applications, it opens a new arena for applications of differential privacy. This article presents a comprehensive survey connecting the basic principles of differential privacy and applications in social network analysis. We concisely review the foundations of differential privacy and the major variants. Then, we discuss how differential privacy is applied to social network analysis, including privacy attacks in social networks, models of differential privacy in social network analysis, and a series of popular tasks, such as analyzing degree distribution, counting subgraphs and assigning weights to edges. We also discuss a series of challenges for future work.
Keyword ：
global sensitivity global sensitivity local differential privacy local differential privacy dependent differential privacy dependent differential privacy subgraph counting subgraph counting degree distributions degree distributions smooth sensitivity smooth sensitivity social network data analysis social network data analysis Differential privacy Differential privacy edge weight query edge weight query
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| GB/T 7714 | Jiang, Honglu , Pei, Jian , Yu, Dongxiao et al. Applications of Differential Privacy in Social Network Analysis: A Survey [J]. | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2023 , 35 (1) : 108-127 . |
| MLA | Jiang, Honglu et al. "Applications of Differential Privacy in Social Network Analysis: A Survey" . | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35 . 1 (2023) : 108-127 . |
| APA | Jiang, Honglu , Pei, Jian , Yu, Dongxiao , Yu, Jiguo , Gong, Bei , Cheng, Xiuzhen . Applications of Differential Privacy in Social Network Analysis: A Survey . | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2023 , 35 (1) , 108-127 . |
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Abstract ：
Vulnerability detection is important for smart contracts because of their immutable and irreversible features. In this work, a new detection method based on adversarial multi-task learning is proposed to improve the accuracy of existing vulnerability detection methods, which is based on the multi-task learning framework, including a shared part and a task-specific part. We optimize the multi-task learning frameworks and propose the mixed parameter sharing method to make each task not only maintain its uniqueness, but also share features with other tasks, which helps solve the problem that the hard parameter sharing method cannot constrain the underlying shared layer and improve the quality of extracted features. In addition, we introduce adversarial transfer learning to reduce noise pollution caused by the private feature and interference between the general feature and the private feature. We experimented on datasets obtained from our previous work, and the experimental results prove that our proposed model can judge whether there are vulnerabilities in smart contracts and then identify their types. Additionally, the results also show that our model effectively improves detection accuracy and has an advantage in performance over representative methods.
Keyword ：
Smart contracts Smart contracts Blockchain security supervision Blockchain security supervision Adversarial transfer learning Adversarial transfer learning Multi-task learning Multi-task learning Vulnerability detection Vulnerability detection
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| GB/T 7714 | Zhou, Kuo , Huang, Jing , Han, Honggui et al. Smart contracts vulnerability detection model based on adversarial multi-task learning [J]. | JOURNAL OF INFORMATION SECURITY AND APPLICATIONS , 2023 , 77 . |
| MLA | Zhou, Kuo et al. "Smart contracts vulnerability detection model based on adversarial multi-task learning" . | JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 77 (2023) . |
| APA | Zhou, Kuo , Huang, Jing , Han, Honggui , Gong, Bei , Xiong, Ao , Wang, Wei et al. Smart contracts vulnerability detection model based on adversarial multi-task learning . | JOURNAL OF INFORMATION SECURITY AND APPLICATIONS , 2023 , 77 . |
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