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Blockchain Technology and Information security (ICBCTIS), International Conference on

作者： Yushu Li Ying Xing Siqi Lu Xiang Deng Heng Pan Xueming Si Cyber Security Academy Zhongyuan University of Technology Zhengzhou China Software College Zhongyuan University of Technology Zhengzhou China Henan Key Laboratory of Network cryptography Zhengzhou China College of Spatiotemporal Big Data Industry Henan Vocational College of Surveying and Mapping Zhengzhou China

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

Smart contracts have brought new developments to Blockchain technology, quickly finding widespread application in finance, law, and business sectors upon their emergence. However, security vulnerabilities in smart contracts have limited their growth. The existing methods for detecting vulnerabilities in smart contracts primarily focus on Ethereum's Solidity, lacking of vulnerability detection tools based on other smart contract languages such as C/C++. Therefore, this paper investigates the current state of vulnerability detection research for smart contract languages other than Ethereum's Solidity and proposes a vulnerability detection method for C/C++ smart contracts based on intermediate value transformation. This method combines Clang technology with the symbolic execution tool KLEE[l], utilizing the Intermediate Representation (IR) from LLVM (Low Level Virtual Machine) for research, andproduces a detection method capable of identifying eight types of code level vulnerabilities. Finally, it summarizes the current shortcomings of smart contract vulnerability detection methods and future research directions.

关键词： Computer languages Codes Smart contracts Information security Finance Maintenance engineering Virtual machines Blockchains Usability Formal verification

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THC-DL: Three-Party Homomorphic Computing Delegation Learning for Secure Optimization

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IEEE Internet of Things Journal 2025年

作者： Yang, Ze Tian, Youliang Xiong, Jinbo Niu, Kun Li, Mengqian Zhang, Xinyu Ma, Jianfeng Guizhou University Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology College of Computer Science and Technology Guiyang550025 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Xidian University State Key Laboratory of Integrated Services Network Xi’an710071 China Xidian University State Key Laboratory of Integrated Services Network School of Cyber Engineering Xi’an710071 China

Delegation Learning(DL) flourishes data sharing, enabling agents to delegate data to the cloud for model training. To preserve privacy, homomorphic encryption (HE) offers an effective solution for privacy-preserving machine learning (PPML) in delegation learning, yet faces critical challenges in functionality (non-linear activation support), practicality (ciphertext blow-up from iterative computations), and security (data leakage risks caused by public knowledge of the mathematical principles applied in model training). To tackle these challenges, we propose THC-DL, a three-party HE framework addressing these challenges holistically for the first time. We elaborately design the ciphertext secure comparison (DL-CSC) protocol to satisfy secure comparison with private inputs, enabling efficient non-linear operations with O(1) communication complexity that reduces runtime to 12.5% of the DGK (Dolev-Greensmith-Kent protocol, the well-known comparison protocol). Second, we construct a Truncation-Mapping (Tru-Map) scheme, a mechanism that transforms input data by truncating and mapping it into a domain that facilitates more efficient processing, and the addition of truncated mapped data to resolve ciphertext blow-up by adaptively scaling ciphertexts during iterative training, ensuring correctness. Third, we formalize data leakage risks in HE-based quadratic convex optimization (standard in ML) and apply THC-DL to construct a secure optimization scheme. Theoretical analysis confirms THC-DL’s resilience against input recovery attacks, even when adversaries exploit public model parameters. Experiments on a real-world platform validate DL-CSC’s efficiency and scalability while reducing the computational complexity and communication complexity from O(n) to O(1) where n denotes the length of the input bits. © 2014 IEEE.

关键词： Convex optimization

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A Botnet data Collection Method for Industrial Internet

A Botnet Data Collection Method for Industrial Internet

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Shu Jian Xian Bo Wang Chenli Lu Jiazhong Huang Yuanyuan Han Shuo School of Cybersecurity Chengdu University of Information Technology Chengdu Sichuan China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu Sichuan China Department of Statistics and Data Sciences Northwestern University Evanston IL USA

Industrial internet security is related to industrial production operations and national economic security. Network intrusion detection dataset in industrial control environment can effectively train detection models and mitigate network attacks, which is an indispensable component for industrial internet security. In this paper, we propose a data collection method for industrial Internet-oriented botnet by classifying traffic packets according to network traffic features and industrial control identification codes, containing the status of industrial control devices. This method can greatly improve the detection accuracy of the model in the industrial internet. To create a botnet incursion dataset in the industrial control environment, we simultaneously employed the existing industrial control devices to gather both industrial control and botnet network traffic. Finally, we used an AI-based model to detect the botnet.

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Dbfl: Dynamic Byzantine-Robust Privacy Preserving Federated Learning in Heterogeneous data Scenario

SSRN

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SSRN 2024年

作者： Chen, Xiaoli Tian, Youliang Wang, Shuai Yang, Kedi Zhao, Wei Xiong, Jinbo State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guizhou University Guizhou Guiyang550025 China College of Mathematics and Statistics Guizhou University Guizhou Guiyang550025 China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350117 China

Privacy Preserving Federated Learning (PPFL) protects the clients' local data privacy by securely uploading encrypted gradients to the server. However, in real-world scenarios, heterogeneous client data distribution makes identifying poisoning gradients challenging. And during local iterations, models moving in different directions constantly shift the boundary between benign and malicious gradients. To address these challenges, we design a Dynamic Byzantine-robust defense strategy (DBFL) based on two-trapdoor homomorphic encryption, achieving detection of encrypted model poisoning attacks. Specifically, we first propose a secure Manhattan Distance method, which accurately measures the difference between each element in two encrypted gradients, thus enabling precise detection of poisoning attacks in heterogeneous data scenarios without exposing any privacy. Furthermore, we design a Byzantine-tolerant aggregation mechanism based on dynamic threshold, where the dynamic threshold changes according to the continuously changing boundary between poisoning and benign gradients in heterogeneous data scenarios. Even when malicious clients controlled by Byzantine attackers constitute 70\% of the DBFL, our method accurately excludes poisoned gradients. security analysis demonstrates that DBFL achieves IND-CPA security. Extensive evaluation on two benchmark datasets shows that DBFL outperforms existing defense strategies. In particular, DBFL achieves a 15\%-40\% accuracy improvement in the non-IID setting compared to existing solutions. © 2024, The Authors. All rights reserved.

关键词： Network security

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Hydamc: A Hybrid Detection Approach for Misuse of Cryptographic Algorithms in Closed-Source Software

Hydamc: A Hybrid Detection Approach for Misuse of Cryptograp...

引用

IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Haoling Fan Fangyu Zheng Jingqiang Lin Lingjia Meng Mingyu Wang Qiang Wang Shijie Jia Yuan Ma State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Cryptography University of Chinese Academy of Sciences Beijing China School of Cyber Security University of Science and Technology of China Hefei China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Cryptographic algorithms are fundamental to secure software development, but security vulnerabilities can arise during implementation, usage, and when calling third-party libraries. As security standards continue to evolve, software updates have become an inevitable trend, and detecting cryptographic algorithm misuse is crucial to ensure compliance with these standards during the update process. However, closed-source software presents challenges in detecting cryptographic algorithm misuse. To enhance the security ecosystem of software, we designed a hybrid detection approach for detecting misuses in closed-source software related to weak cryptographic algorithms, short keys, insecure working modes, and insecure padding modes. Our hybrid detection tool uses both static and dynamic detection methods to collect log information through a logging mechanism in binary executable files. The collected data is cleaned using a data cleaning strategy and analyzed to extract key features, generating test reports to help developers and experts identify cryptographic algorithm security issues. We tested 24 software applications from app stores and found that 62.5% had weak algorithm implementations or usage, 83.3% supported short keys, and 50% supported insecure padding modes. Finally, we provided actionable recommendations to mitigate identified issues.

关键词： Privacy Heuristic algorithms Software algorithms Ecosystems Feature extraction Market research Software

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TaintSE: Dynamic Taint Analysis Combined with Symbolic Execution and Constraint Association

TaintSE: Dynamic Taint Analysis Combined with Symbolic Execu...

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Chenghua Tang Xiaolong Guan Mengmeng Yang Baohua Qiang Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin Guangxi China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin Guangxi China Data61 Commonwealth Scientific and Industrial Research Organization Melbourne VIC Australia School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi China

In order to solve the problem of under-tainting caused by insufficient coverage in dynamic taint analysis and the inability to perform fine-grained level analysis, a dynamic taint analysis method combining symbolic execution and constraint association is proposed. First, through code coverage to guide symbolic execution path exploration and test case generation, code coverage of dynamic taint analysis is improved. Next, perform constraint association based on the corresponding taint constraint transfer rules. Finally, the generation of taint summaries in dynamic taint analysis is completed based on constraint associations, reducing the time consumption in the analysis process. This paper designs and implements a dynamic taint analysis tool TaintSE based on the above methods. The experimental results show that TaintSE effectively improves the code coverage of dynamic taint analysis, and reduces the time required for analysis while ensuring the accuracy of analysis results. In the BugBench test set, TaintSE's analysis path coverage increased by 24% −35% compared to the dynamic taint analysis tool Libdft. In addition, based on the results of taint analysis, the accuracy and recall of taint markers calculated are better than those of Libdft, while reducing the analysis time consumption by about 20%.

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A Multidimensional data Utility Evaluation and Pricing Scheme in the Big data Market

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Chen, Yuling Bai, Rui Wu, Yongtang Li, Tao Zhou, Hui State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Shandong Weifang262700 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Weifang Key Laboratory of Blockchain on Agricultural Vegetables Shandong Weifang262700 China

Big data as a derivative of information technology facilitates the birth of data trading. The technology surrounding the business value of big data has come into focus. However, most of the current research focuses on improving the performance of big data analytics algorithms. data pricing is still one of the main issues in data trading. Therefore, we aim to tackle the problem of evaluating the utility of data in the big data trading market and the problem of maximizing the profits of the various roles involved in data trading. To this end, we propose a Multidimensional data Utility Evaluation (MDDUE) method through three data quality dimensions, namely, data size, availability, and completeness. Next, we propose a big data trading market model including data providers, service providers, and service users. An optimal data-pricing scheme based on a three-party Stackelberg game is proposed to maximize the participants' profits. Finally, a machine learning model is used to verify the rationality and validity of the MDDUE. The results show that MDDUE can evaluate the utility of data more accurately than previous work. The existence and uniqueness of the Nash equilibrium are demonstrated through numerical experiments. © 2023 Yuling Chen et al.

关键词： Big data

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A Secure Authentication Framework to Guarantee the Traceability of Avatars in Metaverse

arXiv

引用

arXiv 2022年

作者： Yang, Kedi Zhang, Zhenyong Tian, Youliang Ma, Jianfeng The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China The Institute of Cryptography & Data Security GuiZhou University Guiyang550025 China The School of Cyber Engineering Xidian University Xi’an710126 China

Metaverse is a vast virtual environment parallel to the physical world in which users enjoy a variety of services acting as an avatar. To build a secure living habitat, it’s vital to ensure the virtual-physical traceability that tracking a malicious player in the physical world via his avatars in virtual space. In this paper, we propose a two-factor authentication framework based on chameleon signature and biometric-based authentication. First, aiming at disguise in virtual space, we propose a chameleon collision signature algorithm to achieve the verifiability of the avatar’s virtual identity. Second, facing at impersonation in physical world, we construct an avatar’s identity model based on the player’s biometric template and the chameleon key to realize the verifiability of the avatar’s physical identity. Finally, we design two decentralized authentication protocols based on the avatar’s identity model to ensure the consistency of the avatar’s virtual and physical identities. security analysis indicates that the proposed authentication framework guarantees the consistency and traceability of avatar’s identity. Simulation experiments show that the framework not only completes the decentralized authentication between avatars but also achieves the virtual-physical tracking. Copyright © 2022, The Authors. All rights reserved.

关键词： Authentication

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Attribute-Based Key Management for Patient-Centric and Trusted data Access in Blockchain-Enabled IoMT

SSRN

引用

SSRN 2023年

作者： Xiang, Axin Gao, Hongfeng Tian, Youliang Wang, Linjie Xiong, Jinbo State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guizhou University Guizhou Guiyang550025 China Network and Information Management Center Guizhou University Guizhou Guiyang550025 China School of Data Science Tongren University Guizhou Tongren554300 China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350117 China

Internet of Medical Things (IoMT) provides real-time data collection and access services to medical entities by connecting their devices to Internet. However, within such a complex architecture, achieving patient-centric data access still presents trustworthiness challenges. Ciphertext-policy attribute-based encryption (CP-ABE) and blockchain are both the promising cryptographic technologies that support patients with secure and fine-grained access control over their data. Nevertheless, both have their unique key management issues. In this paper, we propose a novel patient-centric and trusted data access (PCT-DA) system in blockchain-enabled IoMT. Firstly, we develop an online/offline encryption and verifiable outsourced decryption CP-ABE scheme to relieves the computation pressure on patients/users. Secondly, we design a flexible attribute-based key management mechanism to overcome the security drawbacks of data encryption keys and user attribute keys. Finally, we build a trusted integrity audit contract to guarantee the incentive fairness of patients/users, allowing them to release/access medical data honestly. Moreover, theoretical analyses prove that the PCT-DA system not only achieves confidentiality, integrity, auditability and forward/backward security, but also guarantees static security under random oracles. Simulation experiments indicate that our proposal reduces the time cost by approximately 14.14 seconds over state-of-the-art and is more flexible in BE-IoMT. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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The Chebyshev Collocation Least Squares Approximation Method for the Audio data 2021

The Chebyshev Collocation Least Squares Approximation Method...

引用

2nd International Conference on Computing and data Science, CONF-CDS 2021

作者： Zhang, Qian Qin, Yonghui Chen, Ziqiang Wen, Nengxiang Guilin University of Electronic Technology Guilin541004 China College of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China Cognitive Radio and Information Processing Key Laboratory of Ministry of Education Guilin University of Electronic Technology Guilin541004 China

The Chebyshev collocation least squares configuration fitting method is investigated for the fitting of audio data. This method couples the least squares method with the Chebyshev-Gauss points. Numerical examples are ... 详细信息

ISBN: (纸本)9781450389570

关键词： Least squares approximations

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