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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid tech.ology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Prediction of the Pore-Pressure Built-Up and Temperature of Fire-Loaded Concrete with Pix2Pix

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computers, Materials & Continua 2024年第5期79卷 2907-2922页

作者： Xueya Wang Yiming Zhang Qi Liu Huanran Wang Mechanics and Materials Science Research Center Ningbo UniversityNingbo315211China Jinyun Institute Zhejiang Sci-Tech UniversityLishui321400China School of Civil Engineering and Architecture Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China

Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concreteis closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve theresolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. Torapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adoptedin this work, which is celebrated for its capabilities in image generation. The open-source dataset used hereinfeatures RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the15 principal variables influencing spalling. After conducting a series of tests with different layers configurations,activation functions and loss functions, the Pix2Pix model suitable for assessing the spalling risk of fire-loadedconcrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model inconcrete parameter prediction are verified by comparing its outcomes with those derived fromthe strong couplingTHC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochromeimages as the initial target for analysis yields more dependable results. This work not only offers valuable insightsfor civil engineers specializing in concrete structures but also establishes a robust methodological approach forresearchers seeking to create similar predictive models.

关键词： Fire loaded concrete spalling risk pore pressure generative adversarial network(GAN) Pix2Pix

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Provable Data Possession with Outsourced Tag Generation for AI-Driven E-Commerce

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computers, Materials & Continua 2025年第5期83卷 2719-2734页

作者： Yi Li Wenying Zheng Yu-Sheng Su Meiqin Tang School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China Department of Computer Science and Information Engineering National Chung Cheng UniversityChiayi621301TaiwanChina Advanced Institute of Manufacturing with High-tech Innovations National Chung Cheng UniversityChiayi621301TaiwanChina Department of Computer Science and Engineering Taiwan Ocean UniversityKeelung202301TaiwanChina School of Integrated Circuits Wuxi Vocational College of Science and TechnologyWuxi214028China

AI applications have become ubiquitous,bringing significant convenience to various *** e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy ***,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI ***,it is essential to verify the integrity of e-commerce *** existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in *** address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded ***,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its ***,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.

关键词： Provable data possession data auditing cloud computing e-commerce bloom filter

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ReQ-tank: Fine-grained Distributed Machine Learning Flow Scheduling Approach 29

ReQ-tank: Fine-grained Distributed Machine Learning Flow Sch...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Quanyi Yan, Kedong Yin, Dan Huang, Chanying Xiao, Shan Yang, Yuxin School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangyin China School of Computer Science and Engineering Nanjing Univ. of Science and Technology Nanjing China School of Computer Science and Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Fiberhome Communication Tech. Co. Wuhan China

ISBN: (纸本)9798350330717

The swift advancement of distributed computing has enhanced the support for big data and massive-scale models. Yet, delivering superior services to manage large and intricate network flows in data center networks remains a formidable challenge. In this paper, we present ReQ-tank, an intricate flow scheduling approach based on a multi-level feedback queue (MLFQ) devised to achieve flow prioritization and efficient flow scheduling. ReQ-tank employs a two-tier scheduling strategy: On the flow scheduling layer, priority queues are segmented into two categories, and the flows within high-priority queues follow a strict priority scheduling, while those in low-priority queues adhere to differential weighted Round-robin scheduling;On the packet scheduling layer, ReQ-tank modifies the priority of initially high-priority re-transmitted packets to facilitate fine-grained data packet scheduling. We carry out simulation experiments on web search workloads and data mining workloads. Experimental results demonstrate that ReQ-tank can curtail packet wait time in the network, significantly truncate the flow completion time (FCT) of delay-sensitive flows and counteract the issue of flow starvation in traditional strict priority queues. Consequently, ReQ-tank is deemed more suitable for complex distributed network applications. © 2023 IEEE.

关键词： Data Center Networking Flow scheduling packet scheduling SDN

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FedFC: An Efficient Personalized Federated Learning Method on Non-iid Data 29

FedFC: An Efficient Personalized Federated Learning Method o...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yang, Yuxin Wang, Yifei Huang, Chanying Li, Qianmu Xiao, Shan Nanjing University of Science and Technology School of Cyber Science and Engineering Nanjing China Nanjing University of Science & Technology School of Computer Science & Engineering Nanjing China Nanjing Univ. of Science & Technology School of Cyber Science & Engineering Nanjing China Fiberhome Communication Tech. Co. Fiberhome R & D Center Wuhan China

ISBN: (纸本)9798350330717

Federated Learning has been widely used due to its ability to train models while ensuring data privacy and security. However, the presence of non-i.i.d. (independent and identically distributed) data among different participating entities leads to significant performance disparities in Federated Learning. In addition, as the essence of Federated Learning involves model training on local devices without centralized data storage, it poses certain challenges in personalized tasks. Traditional centralized machine learning methods can perform deep learning and personalized model training on centralized data, while distributed data in Federated Learning may not provide enough information to support personalized requirements. In this paper, we propose a personalized Federated Learning method called FedFC, which adopts parameter decoupling to address the data domain shift issue in Federated Learning. We experimentally validate our proposed solutions on the OrganCMNIST and COVID-19 datasets. The experimental results show that compared to existing methods, FedFC achieves average accuracy improvements of 4.93% and 6.31% on the two datasets, respectively. Additionally, by employing partial gradient uploading, we successfully reduce the communication overhead by 22.67% and 26.91% for each dataset. © 2023 IEEE.

关键词： Efficiency Federated Learning Parameter Decoupling Personalization

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DLSCA: Improving Cross-Device Side-Channel Analysis Using Device Discrepancy Correction 9

DLSCA: Improving Cross-Device Side-Channel Analysis Using De...

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9th IEEE Smart World Congress, SWC 2023

作者： Hu, Fanliang Li, Yi Tan, Haowen Jiang, Shan Xiao, Zhiyuan Nanjing University of Information Science and Technology School of Computer Science Nanjing China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou China

ISBN: (纸本)9798350319804

In this paper, we focus on the problem of Transfer Learning (TL) in Side-Channel Analysis (SCA) due to differences between the source device and the target device. Such differences are inevitable in practical SCA, but have often been disregarded in recent research. Therefore, the impact of device variation on cross-device SCA is analyzed. A method for Device Discrepancies Correction (DDC) is proposed, which can significantly reduce the discrepancy in sidechannel measurements caused by device variations. This is achieved by calculating the covariance shift between the source device and the target device, and then correcting the side-channel measurements of the target device accordingly. The approach is evaluated on eight Atmel XMEGA128A1U8 microcontrollers as a data pre-processing stage for cross-device SCA, and experimental results show that it can further improve the efficiency of existing cross-device SCA models. © 2023 IEEE.

关键词： Cross-device attack Deep learing Device Discrepancy Correction Side-channel analysis Transfer learning

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Next-Generation Consumer Electronics Data Auditing Scheme Toward Cloud-Edge Distributed and Resilient Machine Learning

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 2244-2256页

作者： Li, Yi Shen, Jian Vijayakumar, Pandi Lai, Chin-Feng Sivaraman, Audithan Sharma, Pradip Kumar Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Tindivanam604001 India National Cheng Kung University Department of Engineering Science Tainan701 Taiwan Netaji Subhas University of Technology Department of Information Technology New Delhi110078 India University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

Distributed and resilient machine learning (DRML) endues next-generation consumer electronics with AI function. Intuitively, AI provides innovative, humanized, convenient applications based on the data extended by next-generation consumer electronics. Cloud-edge computing is an ideal undertaken architecture of DRML due to its distributed property. However, as one of the core elements driving AI applications, data could be lost or corrupted owing to damaged electronics, unstable communication, and even cloud providers' malicious behavior. It is essential to ensure the data integrity of next-generation electronics before AI applications. To this end, we proposed a privacy-protection distributed data auditing scheme for cloud-edge DRML. An efficient data integrity verification method that only uses algebraic operation is constructed. Then, Function Secret Share (FSS) extends the data integrity verification method to protect consumer privacy. Besides, a consensus for data auditing results is designed among the edge servers. Finally, we present an abundance of theoretical analyses and experimental findings to substantiate and validate the efficiency and effectiveness of our proposed scheme. © 1975-2011 IEEE.

关键词： Edge computing

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Multi-server assisted data sharing supporting secure deduplication for metaverse healthcare systems

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Future Generation computer Systems 2023年 140卷 299-310页

作者： Zhang, Tao Shen, Jian Lai, Chin-Feng Ji, Sai Ren, Yongjun School of Computer Science Nanjing University of Information Science & Technology China School of Information Science and Technology Zhejiang Sci-Tech University China Department of Engineering Science National Cheng Kung University Taiwan Taizhou University China

The integration of Metaverse and healthcare will improve the allocation and utilization of healthcare resources. Metaverse healthcare data is stored on a public server and downloaded using Artificial Intelligence devices. Patients are given a diagnosis promptly through Metaverse healthcare systems. However, storing Metaverse healthcare data on public servers leads to a variety of problems, such as leakage of patient health information and loss of vital medical record data. Attribute-based encryption (ABE) has played an important role in securely and efficiently Metaverse healthcare data sharing. In this paper, a feature-rich and efficient ABE-based Metaverse healthcare data sharing scheme is presented, which realizes constant encryption computation overhead according to a multi-server structure. To reduce the number of invalid ciphertexts and achieve secure deduplication, Metaverse healthcare ciphertext validity and equivalence detection are also provided in this scheme. Furthermore, a novel attribute-based re-encryption is proposed for authority delegation after deduplication. Finally, the experiment simulated on the PBC library shows that the proposed scheme possesses good performance. The freshness of Metaverse healthcare data and the utilization of the medical center's private server are ensured in this paper. © 2022 Elsevier B.V.

关键词： Diagnosis

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Frequency space mamba enhanced bidirectional generative network for dual-source CBCT 14

Frequency space mamba enhanced bidirectional generative netw...

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Optics in Health Care and Biomedical Optics XIV 2024

作者： Liu, Chang Zhang, Yikun Tao, Yiyuan Lyu, Tianling Xi, Yan Chen, Yang School of Computer Science and Engineering Southeast University Jiangsu Nanjing China Ministry of Education China Zhejiang Lab Zhejiang Hangzhou China Shanghai First-Imaging Tech. Shanghai China

ISBN: (纸本)9781510682122

As DECT becomes widely accepted in the field of diagnostic radiology, there is growing interest in using dual-energy imaging to improve other scenarios. In this context, a new mobile dual-source dual-energy CBCT is being developed for scenarios such as radiotherapy and interventional radiology. The device performs dual-energy measurements by utilizing two X-ray sources mounted side-by-side in the z-axis direction, causing the problem of a mismatch in the fields of view of high-energy and low-energy sources in the z-axis. To solve this problem, this study proposes a method based on deep learning to generate high-energy and low-energy CT images in the missing fields of view. This method can generate high-energy (or low-energy) images from low-energy (or high-energy) images, and then complete the information in the missing fields of view. Furthermore, to enhance the quality of the generated images, a plug-and-play frequency-domain Mamba module is designed to extract frequency-domain features in the latent space, and then the redundant feature maps are filtered out through the designed frequency channel filtering module so that the model can pay more attention to learn and extract the effective features. Experimental results on the simulated data show that the proposed method can effectively generate the missing low- and high-energy CT images, and the SSIM, PSNR, and MAE are up to 99.3%, 48.1dB, and 6.3HU, respectively. Moreover, the generated images could maintain good continuity in the z-axis, which means that our method can effectively ensure the consistency in the fields of view of dual sources. In addition, our model can be further fine-tuned online using the paired dual-energy data in the overlap fields of view when dealing with data from unseen patients, constructing the patient-specific model to ensure the robustness against different samples. © 2024 SPIE.

关键词： computerized tomography

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Investigating the Impact of Bug Dependencies on Bug-Fixing Time Prediction 17

Investigating the Impact of Bug Dependencies on Bug-Fixing T...

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17th ACM/IEEE International Symposium on Empirical Software engineering and Measurement, ESEM 2023

作者： Li, Chenglin Zhao, Yangyang Yang, Yibiao Zhou, Yuming Nie, Liming Ding, Zuohua School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou China Nanjing University State Key Lab for Novel Software Technology Nanjing China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665452236

Background: Bug dependencies refer to the link relationships between bugs and related issues, which are commonly observed in software evolution. It has been found that bugs with bug dependencies often take longer time to be resolved than other bugs without any dependencies. Despite the potential impact of bug dependencies on bug-fixing time, previous studies use traditional metrics without considering bug dependencies to build bug-fixing time prediction models. As a result, there is currently little empirical evidence to support the use of bug dependencies in improving prediction accuracy. Aims: We aim to conduct a comprehensive empirical study to investigate the value of considering bug dependencies for bug-fixing time prediction. Method: We define a set of bug dependency metrics based on bug dependencies. We first investigate the correlation between bug dependency metrics and bug-fixing time to investigate whether bugs with more complex dependencies are more time-consuming to be fixed. Next, we employ principal component analysis to study whether bug dependency metrics capture additional dimensions of a bug compared to traditional metrics. Finally, we build multivariate prediction models to explore whether considering bug dependencies can improve the effectiveness of bug-fixing time prediction. Results: The experimental results suggest that: (1) bugs with more complex dependencies require more time to be fixed;(2) bug dependency metrics are complementary to traditional metrics;(3) considering bug dependencies can improve the effectiveness of bug-fixing time prediction. Conclusions: These findings highlight the importance of considering bug dependencies in bug-fixing time prediction, and provide valuable insights into the potential impact of bug dependencies on software development processes. © 2023 IEEE.

关键词： Principal component analysis

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