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Homomorphic Encryption based stem cell assay data sharing study

Homomorphic Encryption based stem cell assay data sharing st...

Information Technology,big data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Hengjiang Xiao Zhihong Liang Yuxiang Huang Mingming Qin Fei Xiong Mengxin Liu School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data Southwest Forestry University School of Economics and Management Southwest Forestry University Kunming China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

With the rapid development of the biomedical field, stem cell therapy has received widespread attention as an innovative therapy with great potential. However, privacy protection and secure sharing of stem cell testing data are still major challenges. In this paper, we propose a homomorphic encryption-based data sharing scheme for stem cell testing, which achieves efficient and secure data sharing with data privacy by designing a system architecture covering data acquisition, encryption, transmission, decryption and analysis. Experimental results show that the scheme can not only effectively protect data privacy, but also support efficient data processing with less impact on system performance. The main contributions of this paper include designing and implementing a stem cell detection data sharing system based on homomorphic encryption, proposing a homomorphic encryption algorithm optimised for the high-dimensional characteristics of stem cell data, and experimentally verifying the security and effectiveness of the system. Future research directions include optimising homomorphic encryption algorithms, expanding application scenarios, integrating multiple privacy protection techniques, and optimising system performance.

关键词： data privacy Privacy System performance Medical treatment Systems architecture Stem cells Homomorphic encryption Protection Information technology Testing

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time 26

Exploring Patent Transformation Event: Forecasting Patent Tr...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Huo, Jinchen Liu, Weidong Li, Yang Cao, Yan Institute of Scientific and Technical Information of China China College of Computer Science Inner Mongolia University China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350331684

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events. © 2023 IEEE.

关键词： Forecasting

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Cross-Store Next-Basket Recommendation 24

Cross-Store Next-Basket Recommendation

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24th IEEE International Conference on data Mining, ICDM 2024

作者： Ma, Liang-Chen Li, Ya Mai, Zi-Feng Liang, Fei-Yao Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is significantly large due to diverse user behaviors. Cross-domain recommendation (CDR) can effectively alleviate this problem in NBR by transferring knowledge across different domains. Nevertheless, these methods often rely on the similarities of overlapping users, which leads to the negative transfer problem and ignores the overlapping items that are general in real-world scenarios like chain stores. In this paper, we provide a clear symbolic definition of cross-store recommendation (CSR) and distinguish it from CDR. We also propose a novel CSNBR model for cross-store next-basket recommendation task. To fully model the transferable collaborative information between two stores, we learn the embeddings of users, baskets, and items by two intra-store bipartite graphs, and use an inter-store unified bipartite graph to transfer the previously learned knowledge. Furthermore, to alleviate the negative transfer problem, we propose to reconstruct the inter-store unified bipartite graph by utilizing user embeddings obtained from the transfer layer and the disentanglement layer. We also employ two sequence encoders to model the historical sequential information at basket-level and item-level. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the CSNBR model. © 2024 IEEE.

关键词： Graph embeddings

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Joint Sparsification and Quantization for Wireless Federated Learning under Communication Constraints 24

Joint Sparsification and Quantization for Wireless Federated...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Su, Junshen Wang, Xijun Chen, Xiang Cheng, Ray-Guang Sun Yat-Sen University School of Electronics and Information Technology Guangzhou China The Chinese University of HongKong Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen China National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan

ISBN: (纸本)9781665496261

Communication efficiency is one of the key bottlenecks in Federated Learning (FL). Compression techniques, such as sparsification and quantization, are used to reduce communication overhead. However, joint designs of these techniques under communication constraints are not well-explored. This paper investigates the joint uplink compression problem in communication-constrained FL systems. We propose a Block-TopK sparsification scheme to reduce the proportion of bits used for locating entries of a sparsified vector. Considering the communication constraints, an optimization formulation is proposed to minimize the compression error. By solving the optimization problem, our joint compression method provides a better trade-off between sparsity budget and bit width. Numerical results demonstrate that our approach achieves 99.96% of baseline accuracy with only 1.56% of the baseline communication overhead when training ResNet-18 on CIFAR-10. © 2023 IEEE.

关键词： Budget control

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Research on Image Enhancement of Rural Orchard Security Monitoring under Low Illumination 6

Research on Image Enhancement of Rural Orchard Security Moni...

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6th International Conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Zhou, Zili Gu, Shenming School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China

ISBN: (纸本)9781450397148

It is great significance to improve the quality of the monitoring image for the low illumination situation and to improve the security of the rural orchard. In this paper, an improved block homomorphic filtering algorithm is proposed to improve the quality of video surveillance image due to low illuminance in real environment. On the basis of image block adaptation and according to different illumination conditions, the filtering effect is improved by adding and adjusting the weight factor of the transfer function, and the blocking edge effect is alleviated by smoothing filtering. In order to solve the problem of low saturation of low illumination image, the image saturation is adjusted hierarchically and the saturation lower than the threshold value is nonlinear pulled up, so as to restore the color saturation of the image as much as possible. The experimental results show that the PSNR, SSIM and IE are improved by 6%, 21% and 4% respectively. Ultimately, it can improve effectively the image quality of orchard video monitoring under low illumination. © 2022 Association for Computing Machinery.

关键词： Orchards

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RecCoder: Reformulating Sequential Recommendation as Large Language Model-Based Code Completion 24

RecCoder: Reformulating Sequential Recommendation as Large L...

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24th IEEE International Conference on data Mining, ICDM 2024

作者： Lai, Kai-Huang Xi, Wu-Dong Xing, Xing-Xing Wan, Wei Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China NetEase Games China UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

In the evolving landscape of sequential recommendation systems, the application of Large Language Models (LLMs) is increasingly prominent. However, current attempts typically utilize general-purpose LLMs, which present a mismatch in capability and a large semantic gap relative to the specialized needs of recommendation tasks. To tackle these issues, we introduce RecCoder, an innovative model that reformulates sequential recommendation as a code completion task. This approach leverages the superior reasoning capability of code LLMs as a backbone, aligning well with the requirements of recommendation systems. To bridge the semantic gap, RecCoder creates extra tokens for each item and employs item content to initialize token embeddings. Furthermore, we have developed a suite of Semantic Adaptation Fine-tuning tasks, tailored to enhance the model's acquisition of both content and collaborative semantic information, thus aligning the model's intrinsic capabilities with the unique demands of recommendation tasks. Through extensive testing on three public datasets, RecCoder has shown remarkable improvements over existing models in terms of recommendation accuracy and efficiency. This success highlights the substantial yet previously underexplored potential of code LLMs in improving recommendation accuracy and efficiency, suggesting a promising new direction for future research in this area. The implementation code is accessible at https://***/AllminerLab/Code-for-RecCoder-master. © 2024 IEEE.

关键词： Semantics

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Efficient Deep learning-based Real-time Migration Strategies for Containers in SDN

Efficient Deep learning-based Real-time Migration Strategies...

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Next Generation data-driven Networks (NGDN), International Conference on

作者： Shan Huang Yanan Liu Yanuo Li Feili He Xiaodong Duan School of Computer Science and Engineering Dalian Minzu University Key Laboratory of Big Data Application Technology of the State Ethnic Affairs Commission Dalian Ethnic Culture Digital Technology Key Laboratory Dalian China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Container technology is widely used and improve the efficiency of container real-time migration has become an important research topic. Existing studies mainly focus on optimizing iterative dumping of containers without fully considering the impact of migration node selection on migration efficiency. Software-defined networking (SDN), achieves centralized control of the network by separating the control plane and the forwarding plane. Applying deep learning to SDN makes tasks such as traffic prediction and network optimization more intelligent. This paper proposes Prediction Model based on CLCB (CNN-LSTM-CBAM) uses real-time collected load data as input, extracts feature components of load data using one-dimensional convolutional neural networks, and then predicts using LSTM. To improve prediction accuracy, a lightweight CBAM attention module is introduced, and the prediction results are output through fully connected layers. The Network Traffic Prediction Model based on GCGRU-Conv (GCN-GRU-CNN) considers both spatial and temporal features of network traffic. GCN and GRU are used to extract spatial and temporal features of network traffic, respectively, and then these features are fused together using convolutional neural networks. To address the gradient problem caused by increasing network depth, a residual network structure is introduced. Experimental results show that the method proposed can effectively reduce the container migration time and has better prediction results.

关键词： Deep learning Costs Telecommunication traffic Containers Predictive models Feature extraction Real-time systems Convolutional neural networks Software defined networking Load modeling

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EFIE: Efficient and Fine-Grained Image Encryption Algorithm With Permutation-Substitution Synchronization

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IEEE Transactions on Consumer Electronics 2025年

作者： Xie, Dong Zhang, Guiting Yang, Menglin Chen, Fulong Wang, Taochun Hu, Peng Anhui Normal University Anhui Provincial Key Laboratory of Industrial Intelligence Data Security Anhui Engineering Research Centers of Medical Big Data Intelligent System School of Computer and Information Wuhu241002 China

Currently, both pixel-level and bit-level image encryption algorithms demonstrate excellent visual performance. However, most high-security algorithms usually involve complex encryption steps, which increase the time complexity. In addition, some algorithms are challenging to adjust flexibly according to specific scenarios, making it difficult to achieve a balance between security and efficiency. In this paper, we propose an efficient and fine-grained image encryption algorithm, named EFIE. The main step of EFIE is the proposed permutation-substitution synchronization procedure, which is much more efficient compared to existing image encryption solutions. To our knowledge, we first introduce the concept of fine-grained encryption, which allows the encryptor to flexibly set the encryption unit (e.g., 1-bit, 2-bit, or 1-pixel). The smaller the length of the encryption unit, the longer the encryption time and the higher the level of security. The experimental results and various security analyses demonstrate that EFIE provides excellent encryption performance. When the encryption unit is 1 bit, encrypting a 512 × 512 color image requires only about 2.34 seconds. © 1975-2011 IEEE.

关键词： Encryption algorithms

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Fuzzy Deduplication Scheme Supporting Pre-verification of Label Consistency 17th

Fuzzy Deduplication Scheme Supporting Pre-verification of L...

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17th International Conference on Provable and Practical Security, ProvSec 2023

作者： Tang, Zehui Zeng, Shengke Li, Tao Cheng, Shuai Zheng, Haoyu School of Computer and Software Engineering Xihua University Chengdu610039 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Computer Science Qufu Normal University Rizhao276825 China

ISBN: (纸本)9783031455124

Efficiently and securely removing encrypted redundant data with cross-user in the cloud is challenging. Convergent Encryption (CE) is difficult to resist dictionary attacks for its deterministic tag. Server-aided mechanism is against such attacks while it may exist collusion. Focus on multimedia data, this paper proposes an efficient and secure fuzzy deduplication system without any additional servers. We also propose a notion of pre-verification of label consistency to compensate for the irreparable post-verification loss. Compared with other fuzzy deduplication schemes, our work has apparent advantages in deduplication efficiency and security based on a natural data set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Cryptography

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Polarization imaging bi-attentional recursive residual super-resolution method 9

Polarization imaging bi-attentional recursive residual super...

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9th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Liu, Jiaqing Li, Yi Liang, Dong Xu, Guoming Zhou, Pucheng Ma, Jian School of Internet Anhui University Hefei China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei China Anhui Provincial Key Laboratory of Polarized Light Imaging Detection Hefei China School of Electrical and Electronics Engineering Anhui Wenda University of Information Engineering Hefei China

ISBN: (纸本)9781510664432

Due to the limitation of the detector, the spatial resolution of the polarization image obtained by space-modulated full-polarization computed imaging is low. In general, there are a lot of valuable high-frequency components in low resolution space, but it is difficult to reconstruct high frequency information by deep learning method. Through the research of the previous super-resolution reconstruction method based on deep learning, it is found that it is difficult to obtain better lifting effect only by stacking residual blocks to build a deeper network. A polarization imaging bi-attention recursive residual network super-resolution reconstruction method is proposed. In the network structure, bi-attention recursive residual group is used as the deep feature extraction module, and the bi-attention module contained in this module is used to adaptively learn image features of different channels and different transformation Spaces in the deep network. In order to provide the concentrated learning efficiency of high-frequency information, jump connections are introduced into the network structure, and the shallow feature extraction and reconstruction of images are completed by a convolution layer and a sub-pixel convolution algorithm respectively. The experimental verification is carried out under the actual imaging system and simulation data, and compared with other methods in terms of visual effect and quantitative results. Experimental results verify that the proposed method can effectively reconstruct the contour structure and texture details of the detection target in subjective image quality, and is superior to the comparison method in objective evaluation index. © 2023 SPIE.

关键词： Feature extraction

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