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9th IEEE International Conference on Big Data security on Cloud, 9th IEEE International Conference on High Performance and Smart Computing and 9th IEEE International Conference on Intelligent Data and security, BigDatasecurity-HPSC-IDS 2023

作者： Yi, Jinghai Wen, Yunhua Donghua University School of Computer Science and Technology Shanghai201620 China Institute of Information Engineering Chinese Academy of Sciences State Key Laboratory of Information Security Beijing100093 China

ISBN: (纸本)9798350312935

Remote backup is an effective alternative to local backup. It overcomes the shortcomings of local backup that is easy to be stolen and tampered with. In order to protect sensitive information, it is usually necessary to encrypt the sensitive information before uploading to the remote server. How to protect the encryption key used in the remote backup scheme is a crucial issue. Liu et al. (IET information security 2017) designed a data backup scheme using secret sharing scheme and mutual authentication scheme. They divided the key into three parts and stored them in the laptop, smart card and server respectively. The key can be reconstructed from any two parts. However, Hu et al. (IET information security 2019) found that Liu et al.'s scheme cannot resist offline password guessing attack, server impersonation attack and user impersonation attack. Then they proposed an enhanced scheme. Unfortunately, we find that Hu et al.'s scheme cannot cope with offline guessing attack, replay attack, denial of service attack. Besides, their scheme does not consider the case that the smart card and the laptop may be tampered with. Moreover, in their scheme, a malicious server may transmit false information to the laptop or smart card which may lead the user to reconstruct an incorrect key. To solve the above problems, we propose an improved data backup scheme based on multifactor authentication. We show that our scheme can resist the above adversary attacks. Besides, our scheme can detect tampering of smart cards and laptops. Moreover, our scheme can quickly detect the incorrect information transmitted by a malicious server. © 2023 IEEE.

关键词： Smart cards

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Verifiable and Forward-Secure Multi-keyword Query in Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Li, Hongjun He, Debiao Feng, Qi Luo, Min Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu China

The Internet of Medical Things (IoMT) plays a pivotal role in modern healthcare systems, enhancing patients' medical experiences and improving the efficiency of public medical services. However, concerns regarding security and privacy may hinder the widespread implementation and development of IoMT in practical applications. Dynamic Searchable Symmetric Encryption (DSSE) can maintain search capabilities on encrypted data, even when files are dynamically added or deleted. Earlier DSSE schemes typically support only single keyword query and provide forward security under the assumption of semi-honest servers, which significantly limits their applicability in real-world scenarios. To resolve these limitations, we propose a forward-secure and verifiable DSSE scheme that supports multi-keyword conjunctive query. Our scheme ensures forward security by utilizing a chain structure and guarantees correctness and completeness through RSA accumulator and Homomorphic Message Authentication Code (HMAC). Furthermore, we integrate the forward index and inverted index to enable efficient retrieval. Through comprehensive security and performance analysis, we demonstrate that our scheme effectively protects users' privacy while maintaining low computation and communication overheads. Finally, some experimental evaluations are conducted to verify both the correctness and efficiency of the proposed scheme. © 2014 IEEE.

关键词： Structured Query Language

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Block-gram: Mining Knowledgeable Features for Smart Contract Vulnerability Detection 7th

Block-gram: Mining Knowledgeable Features for Smart Contrac...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Li, Tao Wang, Haolong Fang, Yaozheng Jian, Zhaolong Wang, Zichun Xie, Xueshuo Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University Tianjin China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031281235

Effective vulnerability detection of large-scale smart contracts is critical because smart contract attacks frequently bring about tremendous economic loss. However, code analysis requiring traversal paths and learning methods requiring many features training is too time-consuming to detect large-scale on-chain contracts. This paper focuses on improving detection efficiency by reducing the dimension of the features, combined with expert knowledge. We propose a feature extraction method Block-gram to form low-dimensional knowledgeable features from the bytecode. We first separate the metadata and convert the runtime code to opcode sequence, dividing the opcode sequence into segments according to some instructions (jump, etc.). Then, we mine extensible Block-gram features for learning-based model training, consisting of 4-dimensional block features and 8-dimensional attribute features. We evaluate these knowledge-based features using seven state-of-the-art learning algorithms to show that the average detection latency speeds up 25 to 650 times, compared with the features extracted by N-gram. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural Computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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How to Construct Public Timeline for RSA-Formed Time-Lock Cryptography 20th

How to Construct Public Timeline for RSA-Formed Time-Lock ...

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20th International Conference on information security and Cryptology, Inscrypt 2024

作者： Jin, Huixuan Peng, Cong Fu, Jintao Luo, Min Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China Henan Key Laboratory of Network Cryptography Technology Henan Zhengzhou China Inspur Academy of Science and Technology Jinan China

ISBN: (纸本)9789819647330

Time-Locked Puzzle (TLP) and timed-release encryption (T-RE) are both time-based locking mechanisms. The former requires the decryptor to perform a predetermined amount of computation, while the latter requires a trusted server to release the decryption information after a preset time, thereby sending information to the future. As research has progressed, many feature-rich TLPs and TREs have emerged. However, TLP and TRE still face issues in practical deployment. The imbalance in computation load with TLP can result in significant computational burdens in many deployment scenarios, while the timed release in TRE necessitates that the server remains online. To address these issues, our goal is to develop a multifunctional solution to offload the computational burden and allow the server to be offline. Our scheme focuses on RSA-based TLPs and builds upon the sequential TRE schemes proposed by Chvojka and Jager et al.(ESORICS’21). We construct a multi-timeline scheme that aggregates multiple functions and allows the server to be offline. The multi-timeline structure proposed in our scheme, consisting of a main timeline, sub-timelines, and reward timelines, allows different timelines to assume different functions, and the main timeline controls the information release of the remaining timelines to achieve an efficient puzzle-solving release mechanism. The reward timeline provides a transfer account for storing bonuses for entities that solve puzzles, which achieves a self-balancing mechanism for computing resources. Due to the completion of a transaction guaranteed by the adaptor signature and smart contract, which ensure the atomicity of the transaction, the scheme achieves a traceable fair trading mechanism and a public verification mechanism. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： NP-hard

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The Utility Optimization of Type I-PTT for Satellite Internet Navigation Location information Protection

The Utility Optimization of Type I-PTT for Satellite Interne...

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2024 International Conference on Satellite Internet, SAT-NET 2024

作者： Qin, Zhixing Zhou, Jiafeng Tian, Mengjie Zhou, Yihui Lu, Laifeng Shaanxi Normal University School of Mathematics and Statistics Xi'an China Shaanxi Normal University School of Computer Science Xi'an China Xidian University Shaanxi Key Laboratory of Network and System Security Xi'an China

ISBN: (纸本)9798350375176

With the realization of direct-to-handset in Huawei smart phones, satellite communication has become a part of daily life, raising more and more concerns on security. Local differential privacy is a promising method for safeguarding user data in satellite communication, such as navigation positioning service system. With the purpose of protecting location data in navigation positioning services, this study focuses on the utility optimization of type I-PTT, optimizing the worst-case variance while ensuring completeness and unbiased estimation. Compared to the Duchi mechanism, we can conclude that at lower levels of privacy, the utility of I-PTT is lower than that of the Duchi mechanism and vice versa. These findings have significant implications for enhancing satellite internet privacy. © 2024 IEEE.

关键词： Differential privacy

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CurMIM: Curriculum Masked Image Modeling

CurMIM: Curriculum Masked Image Modeling

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Hao Wang, Kun Han, Yudong Wang, Haocong Hu, Yupeng Wang, Chunxiao Nie, Liqiang School of Software Shandong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798350368741

Masked Image Modeling (MIM), following "mask-and-reconstruct" scheme, is a promising self-supervised method to learn scalable visual representation. Studies indicate that selecting an effective mask strategy is vital for MIM. However, existing approaches often rely on static pre-defined priors, which limit their ability to adapt mask strategies dynamically for network optimization. In this paper, we focus on the learning process of the network and introduce human-like curriculum into MIM for dynamic representation refinement, and propose an end-to-end framework Curriculum Masked Image Modeling (CurMIM). CurMIM consists of two components: Mask Priority Measurer, which acts as a curriculum learner to determine mask priority values using the network's intrinsic state information, and Dual Adaptive Selector, which serves as a curriculum scheduler to create effective masks based on these values. With negligible extra parameters, our curriculum-based method consistently establishes noticeable improvements across varying model sizes and benchmarks, showing effectiveness and generalization. © 2025 IEEE.

关键词： Curriculum Learning Image Representation Masked Image Modeling Visual Pre-training

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TMAS:A transaction misbehavior analysis scheme for blockchain

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Blockchain(Research and Applications) 2024年第3期5卷 42-52页

作者： Shiyong Huang Xiaohan Hao Yani Sun Chenhuang Wu Huimin Li Wei Ren Kim-Kwang Raymond Choo School of Computer Science China University of GeosciencesWuhan430074China Artificial Intelligence Thrust Information HubHong Kong University of Science and TechnologyGuangzhou511400China Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Institute of Beihang UniversityKunming650233China Fujian Key Laboratory of Financial Information Processing Putian UniversityPutian351100China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX 78249-0631USA

Blockchain-based cryptocurrencies,such as Bitcoins,are increasingly ***,the decentralized and anonymous nature of these currencies can also be(ab)used for nefarious activities such as money laundering,thus reinforcing the importance of designing tools to effectively detect malicious transaction *** this paper,we propose TMAS,a transaction misbehavior analysis scheme for blockchain-based ***,the proposed system includes ten features in the transaction graph,two heuristic money laundering models,and an analysis method for account linkage,which identifies accounts that are distinct but controlled by an identical *** evaluate the effectiveness of our proposed indicators and models,we analyze 100 million transactions and compute transaction features,and are able to identify a number of suspicious ***,the proposed methods can be applied to other cryptocurrencies,such as token-based cryptocurrencies(e.g.,Bitcoins)and account-based cryptocurrencies(e.g.,Ethereum).

关键词： Blockchain Anti-money laundering Bitcoin Cyptocurrency

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Denoising Low-Dose CT Images Using a Multi-Layer Convolutional Analysis-Based Sparse Encoder network 15

Denoising Low-Dose CT Images Using a Multi-Layer Convolution...

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15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022

作者： Kang, Yanqin Liu, Jin Liu, Tao Qiang, Jun College of Computer and Information Anhui Polytechnic University Wuhu China Ministry of Education Key Laboratory of Computer Network and Information Nanjing China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Imaging in the field of low-dose computed tomography (LDCT) tend to be rather noisy and artificial but is diagnostically useful. One approach to improve the quality of LDCT images is to use deep learning (DL) techniques. DL-based methods produce state-of-the-art performance in low-level medical image restoration tasks but remain defect to interpret due to their black-box constructions. In this paper, we present a simple yet effective LDCT image denoising model by combining the advantages of a residual strategy and a multilayer convolutional analysis-based sparse encoder (CASE). Inspired by convolutional sparse coding (CSC), we constructed a multilayer CASE to sufficiently capture and represent hierarchical image features and designed CASE-net to achieve improved LDCT noise artifact suppression. Moreover, a hybrid loss function, e.g. mean absolute error (MAE) loss, edge loss and perceptual loss, was used to achieve better denoising effects. Experiments on the MAYO and UIH datasets demonstrated the performance of our framework. The results prove that the proposed approach can restrain noise and artifacts and maintain tissue structure during the LDCT imaging. © 2022 IEEE.

关键词： Multilayers

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Malicious Client Detection with Differential Privacy Constraints Based on Model Update Slicing and K-Means Clustering

Malicious Client Detection with Differential Privacy Constra...

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Frontiers Technology of information and computer (ICFTIC), IEEE International Conference on

作者： Mengshi Qi Xin Wang Yunpeng He Ming Yang Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Federated learning (FL) is a decentralized machine learning technique that protects data privacy by trainng models locally on devices and sharing only model updates instead of raw data. However, FL also faces multiple security challenges. Clientside poisoning attacks can compromise global model training by injecting malicious updates. Additionally, the server may infer private information from client model updates. Most existing studies address these two risks separately, lacking a systematic analysis and response to their combined threats. This paper proposes a method called Sliced-Clustering Guard (SCGuard), which slices gradients and performs clustering analysis on each segment, enabling the server to more accurately detect malicious updates and effectively differentiate malicious client updates with differential privacy (DP) protection. Experimental results demonstrate that SCGuard can mitigate poisoning attacks while effectively safeguarding clients' sensitive information.

关键词： Training Privacy Differential privacy Systematics Federated learning Scalability Data models Servers Protection Faces

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