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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Zhang, Lingyun Chen, Yuling Qian, Xiaobin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security Guilin China Weifang University of Science and Technology Blockchain Laboratory of Agricultural Vegetables Shandong WeiFang China Guizhou CoVision Science & Technology Co. Ltd Guiyang China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Ensuring data rights, openness and transaction flow is important in today's digital economy. Few scholars have studied in the area of data confirmation, it is only with the development of blockchain that it has started to be taken seriously. However, blockchain has open and transparent natures, so there exists a certain probability of exposing the privacy of data owners. Therefore, in this paper we propose a new measure of data confirmation based on Ciphertext-Policy Attribute-Base Encryption(CP-ABE). The information with unique identification of the data owner is embedded in the ciphertext of CP-ABE by paillier homomorphic encryption, and the data can have multiple sharers. No one has access to the plaintext during the whole confirmation process, which reduces the risk of source data leakage. © 2022 IEEE.

关键词： Cryptography

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Concept and Construction of Group Signature with Self-Proof Capacity for Confirming and Denying

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International Journal of Network security 2024年第6期26卷 1063-1070页

作者： Cheng, Xiaogang Guo, Ren College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen361021 China College of Business Administration Huaqiao University Quanzhou362021 China

A new extension of group signature is presented, namely GS-SPCCD (Group Signature with Self-Proof Capacity for Confirming and Denying). The actual signer of a legitimate group signature can prove that he indeed signs the signature, while any other group member can prove that he does not sign the signature. The confirming proof can be used to claim a bounty for providing clues to the police, and denying proof can be used to prove one’s innocence in a criminal investigation for rumor-mongering. We also show how to construct a concrete GS-SPCCD scheme based on RSA, modular polynomial, and non-interactive zero-knowledge proof. In our scheme, a non-signer can generate a denying proof without the help of the group manager. © (2024), (International Journal of Network security). All rights reserved.

关键词： Authentication

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Concept and Construction of Group Signature with self-proof capacity for confirming and denying

arXiv

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arXiv 2023年

作者： Xiao-Gang, Cheng Ren, Guo College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen361021 China College of Business Administration Huaqiao University Quanzhou362021 China

With privacy-preserving and traceability properties, group signature is a cryptosystem with central role in cryptography. And there are lots of application scenarios. A new extension concept of group signature is presented, namely group signature with self-proof capacity. For a legitimate group signature, the real signer can prove that the signature is indeed signed by him/her. While for the other members of the group, they can prove that the signature is not signed by him/her. The former can be used for claiming money reward from the police, while the latter can be used for proving one's innocent in a criminal investigation. Copyright © 2023, The Authors. All rights reserved.

关键词： Privacy-preserving techniques

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DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs

arXiv

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arXiv 2024年

作者： Luo, Yingsong Chen, Ling State Key Laboratory of Blockchain and Data Security College of Computer Science and Technology Zhejiang University Hangzhou China

Large language models (LLMs) excel in various tasks but face deployment challenges due to hardware constraints. We propose density-aware post-training weight-only quantization (DAQ), which has two stages: 1) density-centric alignment, which identifies the center of high-density weights and centers the dynamic range on this point to align high-density weight regions with floating-point high-precision regions;2) learnable dynamic range adjustment, which adjusts the dynamic range by optimizing quantization parameters (i.e., scale and zero-point) based on the impact of weights on the model output. Experiments on LLaMA and LLaMA-2 show that DAQ consistently outperforms the best baseline method, reducing perplexity loss by an average of 22.8% on LLaMA and 19.6% on LLaMA-2. Our code is available at https://***/LuoYingSong/DAQ. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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BitPipe: Bidirectional Interleaved Pipeline Parallelism for Accelerating Large Models Training

arXiv

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arXiv 2024年

作者： Wu, Houming Chen, Ling Yu, Wenjie State Key Laboratory of Blockchain and Data Security College of Computer Science and Technology Zhejiang University Hangzhou China

With the increasing scale of models, the need for efficient distributed training has become increasingly urgent. Recently, many synchronous pipeline parallelism approaches have been proposed to improve training throughput. However, these approaches still suffer from two major issues, i.e., pipeline bubbles caused by periodic flushing and extra communication due to the increasing number of pipeline stages. To this end, we propose BitPipe, a bidirectional interleaved pipeline parallelism for accelerating large models training. Specifically, a hybrid scheme of fusing interleaved pipelines with bidirectional pipelines is proposed to reduce the computational time of each single micro-batch and multiply the number of devices executing simultaneously. A V-shaped schedule with eager gradient synchronization is introduced to reduce and overlap the communication between devices. Experiments conducted on up to 32 GPUs show that BitPipe improves the training throughput of GPT-style and BERT-style models by 1.05×-1.28× compared to the state-of-the-art synchronous approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Program processors

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Code Search Is All You Need? Improving Code Suggestions with Code Search 24

Code Search Is All You Need? Improving Code Suggestions with...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Chen, Junkai Hu, Xing Li, Zhenhao Gao, Cuiyun Xia, Xin Lo, David School of Software Technology Zhejiang University Ningbo China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Concordia University Montreal Canada Harbin Institute of Technology Shenzhen China Zhejiang University Hangzhou China Singapore Management University Singapore

ISBN: (纸本)9798400702174

Modern integrated development environments (IDEs) provide various automated code suggestion techniques (e.g., code completion and code generation) to help developers improve their efficiency. Such techniques may retrieve similar code snippets from the code base or leverage deep learning models to provide code suggestions. However, how to effectively enhance the code suggestions using code retrieval has not been systematically investigated. In this paper, we study and explore a retrieval-augmented framework for code suggestions. Specifically, our framework leverages different retrieval approaches and search strategies to search similar code snippets. Then the retrieved code is used to further enhance the performance of language models on code suggestions. We conduct experiments by integrating different language models into our framework and compare the results with their original models. We find that our framework noticeably improves the performance of both code completion and code generation by up to 53.8% and 130.8% in terms of BLEU-4, respectively. Our study highlights that integrating the retrieval process into code suggestions can improve the performance of code suggestions by a large margin. © 2024 ACM.

关键词： Deep learning

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Enhancing Reentrancy Vulnerability Detection and Repair with a Hybrid Model Framework

Enhancing Reentrancy Vulnerability Detection and Repair with...

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Asia-Pacific Conference on Software Engineering

作者： Mengliang Li Xiaoxue Ren Han Fu Zhuo Li Jianling Sun State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security College of Computer Science and Technology Zhejiang University State Street Technology(Zhejiang) Ltd

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

Smart contracts bring revolutionary changes to the credit landscape. However, their security remains intensely scrutinized due to numerous hacking incidents and inherent logical challenges. One well-known and representative issue is reentrancy vulnerability, exemplified by DAO attacks that lead to substantial economic losses. Conventional approaches to detecting and repairing reentrancy vulnerability often suffer from numerous limitations, including disregarding the intricate vulnerability features and the overfitting problems associated with imbalanced datasets. Large language models are distinguished for their excellent language understanding and have achieved explosive success in artificial intelligence. However, direct prompt-based LLMs-driven approaches for reentrancy vulnerability are plagued by inefficiencies and a lack of domain-specific vulnerability knowledge. This paper proposes a hybrid framework to enhance reentrancy vulnerability detection and repair and safeguard smart contract security. This unified framework comprises two crucial modules: enhanced DL-driven vulnerability detection and knowledge-aware LLMs-driven vulnerability repair. Our approach can significantly enhance reentrancy vulnerability detection and repair efficiency by integrating advanced techniques such as feature extraction, data balancing, deep learning networks, and knowledge-aware prompting. Extensive experimental results validate the superiority of our approach over state-of-the-art baselines, emphasizing its potential to fortify the security of smart contracts and blockchain-based systems. For instance, our approach can achieve 3.51 %, 2.31 %, 0.42%, and 0.85 % improvements in accuracy, recall, precision, and F1 score while detecting reentrancy vulnerability. Additionally, our approach also can achieve a 9.62% improvement in reentrancy vulnerability repair.

关键词： Deep learning Codes Large language models Smart contracts Maintenance engineering Syntactics Feature extraction Classification algorithms security Software engineering

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Optimizing Potato Disease Detection in Pakistan with Machine Learning: A Comparative Analysis 3

Optimizing Potato Disease Detection in Pakistan with Machine...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Ahmed, Irfan Khalil, Manahal Ghulam, Safoora Mahmood, Jabar Batool, Khadija Khalid, Nibtal Arshad, Pakiza Dept of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science & Technology Zhejiang University Hangzhou China Dept of Computer Science Int. Institute of Science Art & Technology Gujranwala Pakistan

ISBN: (纸本)9798331504861

The agricultural sector of Pakistan depends heavily on the production of potatoes, however diseases like Bacterial Wilt, Late Blight, and Early Blight are posing a growing danger to this industry since they can negatively affect crop productivity and food security. Detecting these illnesses using traditional methods is time-consuming, labor-intensive, and sometimes it requires specialized knowledge which is not easy for farmers. The implementation of a machine learning (ML) framework for illness identification using the methods of K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) overcomes these challenges. This study utilizes data mining and preprocessing strategies to improve the accuracy of models and adaptability on a dataset of potato plants’ photos gathered across diverse areas in Pakistan. The models are assessed using performance criteria such computational efficiency, accuracy, precision, recall and F1-score. Random Forest model having 97% accuracy outperforms the other ML models in identifying plant disease. The suggested approach provides a practical, affordable, and effective way to identify diseases in a timely manner, allowing farmers to make informed decisions to lower crop losses. The aim of the study is to highlight how ML has the potential to transform Pakistani agriculture, enhancing food security and promoting long-term economic prosperity. ©2024 IEEE.

关键词： Random forests

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A Privacy-preserving Location Recommendation Scheme without Trustworthy Entity

A Privacy-preserving Location Recommendation Scheme without ...

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

作者： Changli Zhou Jing Peng Ying Ma Qingfeng Jiang College of Computer Science and Technology Huaqiao University Xiamen PR China Key Laboratory of Data Mining and Intelligent Recommendation Fujian Province University Xiamen PR China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen China College of Computer Science and Engineering Changshu Institute of Technology Changshu PR China

ISBN: (纸本)9781665416597

The widespread application of location recommendation service brings convenience and social fun to users, but the misuse of user check-in data and location recommendation results can bring serious privacy leakage, because these check-in data and location recommendation results imply private information of users' interests and behavioral hobbies. To address the check-in privacy and recommendation result privacy leakage issues in existing location recommendation schemes, we first propose a location recommendation algorithm based on the similarity of user historical check-ins, and then we devise three location recommendation protocols on ciphertext to protect the privacy of user check-in data and recommendation results based on the homomorphic property of Paillier cryptosystem without assuming any trustworthy entity. These protocols calculate the ciphertexts of recommendation results at the location recommendation server(LSP) without learning any explicit check-in privacy of the user, and it also guarantees that the decryption server(DS) can only implement the decryption operations as requested by the user without being aware of any decrypted recommendation results, so that the privacy of user check-in data and recommendation results are both preserved solidly in our protocols. Although the above recommendation process is carried out without any user's explicit data, it is still effective enough to guarantee the accuracy of recommendation results. Finally, we prove the security, efficiency and accuracy of our scheme by extensive performance analysis.

关键词： Privacy data privacy Protocols Conferences Prediction algorithms Performance analysis Cryptography

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Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection 31

Towards Effective and Efficient Error Handling Code Fuzzing ...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Chen, Kang Wen, Ming Jia, Haoxiang Wu, Rongxin Jin, Hai Wuhan430074 China Jin YinHu Laboratory Wuhan430074 China School of Informatics Xiamen University Xiamen361005 China School of Computer Science and Technology HUST Wuhan430074 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China

ISBN: (纸本)9798350330663

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL-F I are effective that contribute significantly to its overall performance. © 2024 IEEE.

关键词： Errors

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