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2022 International Conference on computer Application and information security, ICCAIS 2022

作者： He, Renjie Zheng, Xianghan Wang, Gong School of Computer and Big Data Fuzhou University Fujian Fuzhou China The Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fujian Fuzhou China

ISBN: (纸本)9781510663459

Data security has always been one of the public concerns, and homomorphic encryption technology, as one of the effective means to ensure data security, has always been a hot topic of academic research. Traditional machine learning is trained in a batch environment. In practice, data owners tend to outsource data and model training tasks to third-party clouds with huge computing resources, but data outsourcing carries the risk of privacy leakage. Therefore, in order to prevent this situation, an effective method is to encrypt the data before the outsourcing, and the third-party cloud trains the machine model of the ciphertext data. This method has special requirements for encryption algorithms which should support computation directly on the ciphertext data. This paper presents a novel data flow computing privacy protection framework based on the homomorphic encryption algorithm CKKS and the flow data processing engine Flink, called SDPPF (Streaming Data Privacy Protection on Flink). The proposed framework supports the functions of data stream encryption, decryption and ciphertext computing, and expands the functions of vector point multiplication and array sum operation on the basic simultaneous operation supported by the original CKKS algorithm. This paper also selects a classical machine learning algorithm: KNN algorithm, combined with the homomorphic encryption algorithm CKKS to realize the privacy protection machine learning algorithm. © 2023 SPIE.

关键词： Cryptography

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Research on Text Mining Technology for Power Grid Safety Hazard Identification

Research on Text Mining Technology for Power Grid Safety Haz...

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2023 IEEE International Conference on Electrical, Automation and computer Engineering, ICEACE 2023

作者： Guo, Yusong Dai, Caiyan Wang, Junliang Feng, Jie State Grid Smart Grid Research Institute Co. Ltd State Grid Key Laboratory of Information & Network Security Beijing China Beijing China State Grid Corporation of China Beijing China

ISBN: (纸本)9798350309614

Text data mining and analysis for power grid safety hidden danger files, and in-depth study of power grid hidden danger investigation standards and norms, can help power grid enterprises to carry out hidden danger management efficiently and conveniently. Firstly, the study explains the content of the hidden problems of power grid enterprises and the direction of the study. It also summarizes the general process and methods of text mining technology, and explores the current research status of text mining technology in grid hidden danger investigation;Secondly, we observe the textual characteristics of the grid hidden danger investigation, get the visualization results as well as the main manifestations of the grid hidden danger by text mining the existing 412 grid hidden danger texts. And we finally explore the difficulties of the grid hidden danger investigation text mining as well as the possible development direction in the future. © 2023 IEEE.

关键词： hidden danger management power grid hazard investigation text mining textual characteristics visualization results

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A Unified Stream and Batch Graph Computing Model for Community Detection 17th

A Unified Stream and Batch Graph Computing Model for Commu...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Dai, Jinkun Wu, Ling Guo, Kun Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

An essential challenge in graph data analysis and mining is to simply and effectively deal with large-scale network data that is expanding dynamically. Although batch-based parallel graph computation frameworks have better accuracy, they cannot process incremental data on time and need to be recomputed. To process real-time data, stream processing applications need to be redeveloped, which increases the redundancy of work, and some existing dynamic graph computation schemes are not generalizable. This paper proposes a unified stream and batch graph computing model(USBGM). The model is compatible with both stream and batch graph computing. Graph operators and algorithms developed based on the model can handle stream and batch graph data in a unified manner. The experiments on real-world and artificial networks verified the effectiveness and efficiency of the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Population dynamics

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Efficient Private Circuits with Precomputation

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2023年第2期2023卷 286-309页

作者： Wang, Weijia Ji, Fanjie Zhang, Juelin Yu, Yu School of Cyber Science and Technology Shandong University Qingdao China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Qingdao China Quan Cheng Shandong Laboratory Jinan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Qi Zhi Institute Shanghai China Shanghai Key Laboratory of Privacy-Preserving Computation Shanghai China

At CHES 2022, Wang et al. described a new paradigm for masked implementations using private circuits, where most intermediates can be precomputed before the input shares are accessed, significantly accelerating the online execution of masked functions. However, the masking scheme they proposed mainly featured (and was designed for) the cost amortization, leaving its (limited) suitability in the above precomputation-based paradigm just as a bonus. This paper aims to provide an efficient, reliable, easy-to-use, and precomputation-compatible masking scheme. We propose a new masked multiplication over the finite field Fq suitable for the precomputation, and prove its security in the composable notion called Probing-Isolating Non-Inference (PINI). Particularly, the operations (e.g., AND and XOR) in the binary field can be achieved by assigning q = 2, allowing the bitsliced implementation that has been shown to be quite efficient for the software implementations. The new masking scheme is applied to leverage the masking of AES and SKINNY block ciphers on ARM Cortex M architecture. The performance results show that the new scheme contributes to a significant speed-up compared with the state-of-the-art implementations. For SKINNY with block size 64, the speed and RAM requirement can be significantly improved (saving around 45% cycles in the online-computation and 60% RAM space for precomputed values) from AES-128, thanks to its smaller number of AND gates. Besides the security proof by hand, we provide formal verifications for the multiplication and T-test evaluations for the masked implementations of AES and SKINNY. Because of the structure of the new masked multiplication, our formal verification can be performed for security orders up to 16. © 2023, Ruhr-University of Bochum. All rights reserved.

关键词： Formal verification

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Block-Streaming Service Loading Optimization of Android Dalvik Executable File for Cloud-End Collaboration

Block-Streaming Service Loading Optimization of Android Dalv...

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2022 International Conference on networking and network Applications, NaNA 2022

作者： Wang, Yichuan Feng, Yanhua Du, Yanning Hei, Xinhong Tian, Yuying Cui, Xiangjie 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: (数字)9781665461313

ISBN: (纸本)9781665461313

In recent years, dynamic loading technologies of Android applications have emerged in an endless stream. At present, the existing Android application loading method is to load the entire Android Dalvik Executable (DEX) file for cloud-end collaboration, and there is a problem of finer-grained loading optimization in the loading process. Based on the analysis of Android DEX file in this paper, a formal model of each class is established by using the analysis result of this file, analyzes the user's behavior, and tries to ensure that the class to be used in high probability is loaded into memory first, and a block-streaming service loading optimization model of Android DEX file is put forward. The validity of the model is verified by quantitative analysis of Android DEX file. Trying to load the whole Dalvik Executable file and the classes needed by the application separately, recording and comparing the storage space and loading time overhead. In order to avoid the uncertain factors of the network environment, the above files have been tested and recorded several times. The result shows that the time and storage space for loading the whole DEX file are far greater than the sum of the classes required by the application. With this model, the fine-grained formal analysis of Android DEX file can be performed, which is of great significance for exploring the loading method of application block flow service. © 2022 IEEE.

关键词： Android (operating system)

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Quantum K-Means Model based on Optimization Framework

Quantum K-Means Model based on Optimization Framework

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Shao, Zexiang Zhang, Shibin Kumar, Sachin School of Cyberspace Security Chengdu University of Information Technology Chengdu610225 China Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu610225 China Ajay Kumar Garg Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9781665457033

The Quantum K-means clustering algorithm offers the advantage of quantum parallel computing, but suffers from issues related to cluster center initialization and sensitivity to noisy data due to its similarity with the K-means clustering algorithm. To address these challenges, we propose a quantum K-means model based on an optimization framework. Specifi-cally, we use a quantum genetic algorithm with pin optimization to adjust the fitness function for the Quantum K-means clustering algorithm. The optimally adapted quantum genetic algorithm reduces the sensitivity of cluster center initialization and improves the noise robustness of the Quantum K-means clus-tering algorithm using a noise reduction self-encoder. As a result, the proposed model achieves better clustering performance even in the presence of noise. Experimental results demonstrate that our proposed quantum K-means model has fewer clustering iterations, lower probability of falling into local optima, and improved noise robustness after optimizing the clustering cen-ters. The model's effectiveness in clustering is also demon-strated experimentally. © 2023 IEEE.

关键词： Noise abatement

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Design and implementation of content confidentiality check system 3

Design and implementation of content confidentiality check s...

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3rd International Conference on Electronic information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Song, Yanyan Chen, Menghong Pi, Chunying Nie, Peng College of Computer Science Nankai University Tianjin300350 China College of Cyber Science Nankai University Tianjin300350 China College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300350 China

ISBN: (纸本)9781510680449

Nowadays, companies use databases for information storage and it is inevitable that there will be confidential information which can cause severe crises and public relations problems if leaked. In order to solve this problem, we designed and implemented a content confidentiality check system for database. In this paper, the first section introduces the module design of the system. The second section introduces the key technology implementation of the system, which includes how to detect sensitive words and user password strength. The third section shows the running interface and detection results of the system. In short, this system helps users to realize a more convenient self inspection of the confidential content stored in their database, aiming to provide a strong guarantee for data storage security. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Database systems

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A Multidimensional Adaptive Perception Model for security Contraband Detection

SSRN

引用

SSRN 2023年

作者： Bian, Weixin Feng, Luo Xu, Deqin Jie, Biao Huang, Yi The School of Computer and Information Anhui Normal University Wuhu241002 China Anhui Province Key Laboratory of Network and Information Security Wuhu241002 China

Identifying prohibited items in security X-ray images is a challenging task. Since prohibited items may be intentionally concealed, and the background of X-ray images is complex and the image representation ability is limited, it is easy to cause missed detection or false alarms. These questions are critical to ensuring safety. To address these challenges, the Multidimensional Adaptive Perception Module (MAPM) is proposed, and coordinated attention (CA) adaptive learning weights are introduced to make the learning focus of the model more accurate. The MAPM module fuses high semantic information into deep networks through residual connections, while integrating detailed features into shallow networks, allowing the model to obtain deeper and more comprehensive information and improve its ability to understand complex backgrounds. At the same time, we adopt a decoupling head mechanism to accelerate the convergence speed of the network. Experiments have proven that our model significantly improves the detection accuracy of X-ray security inspection images. Compared with the YOLOv5s model, the average accuracy is increased by 7.9mAP. Compared with many current advanced algorithms, our model has higher accuracy and stronger robustness. This research aims to provide an effective and reliable solution to the security inspection field to improve safety and operability. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Research on iOS Remote security Access Technology Based on Zero Trust 6

Research on iOS Remote Security Access Technology Based on Z...

引用

6th IEEE information Technology and Mechatronics Engineering Conference, ITOEC 2022

作者： Fang, Wengao Guan, Xiaojuan Global Energy Interconnection Research Institute Co. Ltd State Grid Key Laboratory of Information Network Security Information Communication Department Jiangsu Province Nanjing China

ISBN: (纸本)9781665431859

Under the situation of regular epidemic prevention and control, teleworking has gradually become a normal working mode. With the development of modern information technologies such as big data, cloud computing and mobile Internet, it's become a problem that how to build an effective security defense system to ensure the information security of teleworking in complex network environment while ensuring the availability, collaboration and efficiency of teleworking. One of the solutions is Zero Trust network(ZTN), most enterprise infrastructures will operate in a hybrid zero trust/perimeter-based mode while continuing to invest in IT modernization initiatives and improve organization business processes. In this paper, we have systematically studied the zero trust principles, the logical components of zero trust architecture and the key technology of zero trust network. Based on the abstract model of zero trust architecture and information security technologies, a prototype has been realized which suitable for iOS terminals to access enterprise resources safely in teleworking mode. © 2022 IEEE.

关键词： Complex networks

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OpenViewer: Openness-Aware Multi-View Learning

arXiv

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arXiv 2024年

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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