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2nd International Conference on Big Data and Privacy computing, BDPC 2024

作者： Wang, YiChuan Yan, YiLiang Liu, XiaoXue Gao, Wen Wang, ZiYan School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9798350307597

Cross chain contact across several blockchains becomes increasingly significant in importance with the increased use of blockchain technology. However, security cannot be assured when producing equivalent outputs through cross-chain transmission because it is simple to launch an assault, and without security measures;it is simple to cause the transmission material to be stolen. This article combines TPM, IBC, and associates encryption and decryption techniques for protection to overcome this issue. We have establishes the viability and efficacy of the suggested method through testing and performance analyses. The experimental findings show that our approach can successfully preserve privacy and guarantee the security of data transfer. © 2024 IEEE.

关键词： Blockchain

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Locally differentially private high-dimensional data synthesis

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Science China(Information Sciences) 2023年第1期66卷 25-42页

作者： Xue CHEN Cheng WANG Qing YANG Teng HU Changjun JIANG Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents Sam Priv Syn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation *** marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible,the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that Sam PrivSyn can not only protect privacy but also retain the correlation information between the attributes.

关键词： local differential privacy high-dimensional data synthesis sampling data privacy privacy-preserving protocols

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A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction 31

A Chain-of-Task Framework for Instruction Tuning of LLMs Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Xinpeng Xu, Bing Yang, Muyun Cao, Hailong Zhu, Conghui Zhao, Tiejun Lu, Wenpeng Faculty of Computing Harbin Institute of Technology Harbin China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (纸本)9798891761964

Over-correction is a critical issue for large language models (LLMs) to address Grammatical Error Correction (GEC) task, esp. for Chinese. This paper proposes a Chain-of-Task (CoTask) framework to reduce over-correction. The CoTask framework is applied as multi-task instruction tuning of LLMs by decomposing the process of grammatical error analysis to design auxiliary tasks and adjusting the types and combinations of training tasks. A supervised fine-tuning (SFT) strategy is also presented to enhance the performance of LLMs, together with an algorithm for automatic dataset annotation to avoid additional manual costs. Experimental results demonstrate that our method achieves new state-of-the-art results on both FCGEC (in-domain) and NaCGEC (out-of-domain) test sets. © 2025 Association for Computational Linguistics.

关键词： Chains

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Threat Model and Defense Scheme for Side-Channel Attacks in Client-Side Deduplication

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Tsinghua Science and technology 2023年第1期28卷 1-12页

作者： Guanxiong Ha Hang Chen Chunfu Jia Mingyue Li College of Cyber Science Nankai UniversityTianjin 300350China Tianjin Key Laboratory of Network and Data Security Technology Tianjin 300350China

In cloud storage,client-side deduplication is widely used to reduce storage and communication *** client-side deduplication,if the cloud server detects that the user’s outsourced data have been stored,then clients will not need to reupload the ***,the information on whether data need to be uploaded can be used as a side-channel,which can consequently be exploited by adversaries to compromise data *** this paper,we propose a new threat model against side-channel *** from existing schemes,the adversary could learn the approximate ratio of stored chunks to unstored chunks in outsourced files,and this ratio will affect the probability that the adversary compromises the data privacy through side-channel *** this threat model,we design two defense schemes to minimize privacy leakage,both of which design interaction protocols between clients and the server during deduplication checks to reduce the probability that the adversary compromises data *** analyze the security of our schemes,and evaluate their performances based on a real-world *** with existing schemes,our schemes can better mitigate data privacy leakage and have a slightly lower communication cost.

关键词： cloud storage deduplication side-channel privacy

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Efficient Multi-keyword Searchable and Verifiable Data Sharing for Cloud-Edge Collaboration Intelligent Transportation Systems

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IEEE Internet of Things Journal 2025年第11期12卷 15869-15882页

作者： Liu, Peng He, Qian Chen, Yiting Jiang, Shan Guilin University of Electronic Technology Department of Guangxi Key Laboratory of Cryptograph and Information Security Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptograph and Information Security Guilin541004 China The Hong Kong Polytechnic University Department of Computing Hong Kong

Intelligent transportation systems (ITS) are essential for the development of future smart cities. They can improve traffic management, mitigate urban congestion, and provide extensive social services by disseminating traffic data collected from *** facilitate the exchange of sensory data with other vehicles and alleviate the local storage load, sensory data from vehicles is frequently uploaded to cloud or edge ***, current ITS data sharing frameworks exhibit certain security vulnerabilities. In particular, they are unable to support secure multi-keyword searches or ensure verifiable retrieval of encrypted information. Moreover, some of the schemes cannot ensure the integrity of the retrieved data. Therefore, we propose an efficient multi-keyword searchable and verifiable data sharing(EMKV-ABSE) for cloud-edge collaboration ITS. EMKV-ABSE integrates a multi-keyword attribute-based searchable encryption scheme and a tamper-proof consortium blockchain. In EMKV-ABSE, we store the keyword indexes and ciphertext hashes of the shared data on the blockchain, which can ensure the integrity and authenticity of the data. Meanwhile, to avoid security and trust issues associated with central search cloud servers, smart contracts are utilized to perform multi-keyword searches and verify retrieval results. Furthermore, the EMKV-ABSE scheme outsources the complex computational tasks in encryption and decryption to edge servers, which realizes lightweight computation for resource-constrained *** security analysis proves that EMKV-ABSE satisfies the indistinguishability under the chosen plaintext attack (IND-CPA) and the chosen keyword attack (IND-CKA) in the standard *** evaluation shows the efficiency and practicability of our scheme. © 2014 IEEE.

关键词： Data integrity

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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

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Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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Evaluation Method of Object Control Capability for Children Based on Nonlinear Dynamics 23

Evaluation Method of Object Control Capability for Children ...

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3rd International Conference on Bioinformatics and Intelligent computing, BIC 2023

作者： Wang, Huaijun Li, Chao Zhang, Dongsheng Li, Junhuai Key Laboratory of Network Computing and Security Technology School of Computer Science and Engineering Xi an University of Technology Xi'an China

ISBN: (纸本)9798400700200

DCD (Developmental Coordination Disorder) has become a global problem, and several studies have shown that early detection and intervention are important for the prevention and treatment of DCD in children. To address the problems of low intelligence, simple assessment indexes and single dimension in assessing children's motor coordination, a method based on non-linear dynamics is proposed to assess children's object control ability. Using the child's in-situ ball tapping as the test item, the Kinect and inertial sensors are used to collect hand spatial position information and human motion biomechanical signals during the ball tapping process. The complexity of the signal is reflected by the sample entropy of the motion signal. The approach suggested in this thesis, which uses non-linear kinetic characteristics, enhances the accuracy of behavioral and developmental identification by 1% to 3% when compared to existing methods. The experiments prove that the proposed method has a better recognition rate and stability than the traditional assessment methods. © 2023 ACM.

关键词： Principal component analysis

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Enhancing Stance Detection on Social Media via Core Views Discovery 27

Enhancing Stance Detection on Social Media via Core Views Di...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Yan, Yu Shen, Yinghan Liu, Teli Jiang, Xuhui Yin, Dechun School of Information and Network Security People Public Security University of China China Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781643685489

Stance detection aims to identify the expressed attitude towards a target from the text, which is significant for learning public cognition from social media. The short and implicit nature of social media users' expressions potentially results in the stance understanding bias of the model. To address this problem, introducing external background information is helpful to mitigate these biases and enhance explainability. The core view, reflecting the motivations and reasons behind an individual's stance toward the target, can be summarized and extracted from collective tweets, which can serve as a reference for stance detection. In this study, we propose the Stance Detection via Core View Discovery (SD-CVM), where the core views are used for background information modeling. Specifically, we construct a joint classifier combining the semantic understanding of tweets and their relevant core views from the public. We utilize the Large Language Model (LLM) to extract core views with stances from tweets and use these core views as background references for tweets. To further optimize the tweet understanding, we develop the contrastive and rebalancing mechanism by incorporating stance supervision signals for training. Experiments on two representative datasets demonstrate the excellent performance of our method. © 2024 The Authors.

关键词： Tweets

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DP-DID: A Dynamic and Proactive Decentralized Identity System

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IEEE Transactions on Information Forensics and security 2025年 20卷 4999-5014页

作者： Yin, Jie Xiao, Yang Chen, Qian Lim, Yong zhi Liu, Xuefeng Pei, Qingqi Zhou, Jianying Xidian University State Key Laboratory of Integrated Services Network Xi’an710071 China Singapore University of Technology and Design Department of Information Systems Technology and Design Tampines 487372 Singapore Xidian University State Key Laboratory of Integrated Services Network Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China

Decentralized identity (DID) is a transformative paradigm that leverages blockchain, decentralized identifiers and verifiable credentials (VCs) to enable self-sovereign and decentralized identity management with myriad application areas. However, existing DID implementations are confronted with two key challenges: insufficient decentralization and vulnerability to mobile adversary attacks. First, they paradoxically introduce central identity resolvers, intermediaries or static committees to manage critical identity services, key management or credential issuance, which violates the decentralized controlling aim against a single point of failure. Second, these systems are vulnerable to mobile adversaries who can gradually compromise multiple nodes or committee members over a long period, eventually seizing control of the system. In this paper, we propose DP-DID, the first dynamic and proactive decentralized identity system specifically designed to resist mobile adversary attacks in dynamic committee settings. To eliminate centralized authorities, DP-DID leverages blockchain, dynamic committees and BLS (Named after Boneh, Lynn, and Shacham) signatures, which achieves decentralization. In addition, we design a dynamic and batch proactive secret sharing (DBPSS) scheme for DP-DID to ensure proactive security against mobile adversary attacks. This is achieved by allowing at most t (threshold) committees to be corrupted per period, with the set of corrupted committees changing dynamically even if all players are eventually compromised. By incorporating DBPSS, DP-DID achieves efficient key management for multiple users in dynamic settings, enhancing overall system scalability. Through rigorous analysis, DP-DID is proven to be forward secure and secure against mobile adversary attacks under a widely adopted malicious model. Extensive experiments show that DP-DID has efficient performance, and our DBPSS scheme outperforms FaB-DPSS by over 11.67× in key handover efficiency. ©

关键词： Decentralized finance

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Never Lost keys:A Novel key Generation Scheme Based on Motor Imagery EEG in End-Edge-Cloud System

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China Communications 2022年第7期19卷 172-184页

作者： Yichuan Wang Dan Wu Xiaoxue Liu Xinhong Hei School of computer science and Engineering Xi’an University of TechnologyXi’an 710048China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an 710048China

Biometric key is generated from the user’s unique biometric features,and can effectively solve the security problems in ***,the current prevailing biometric key generation techniques such as fingerprint recognition and facial recognition are poor in randomness and can be forged *** to the characteristics of Electroencephalographic(EEG)signals such as the randomness,nonlinear and non-stationary etc.,it can significantly avoid these *** paper proposes a novel method to generate keys based on EEG signals with end-edgecloud collaboration *** sensors to measure motor imagery EEG data,the key is generated via pre-processing,feature extraction and *** show the total time consumption of the key generation process is about *** scheme is practical and feasible,which provides a research route to generate biometric keys using EEG data.

关键词： EEG biometric key generation end-edgecloud system information security

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