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A certificateless and KGA-secure searchable encryption scheme with constant trapdoors in smart city

Digital Communications and Networks 2024年

作者： Hongjun Li Debiao He P. Vijayakumar Fayez Alqahtani Amr Tolba School of Cyber Science and Engineering Wuhan University Wuhan 430072 China Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Department of Computer Science and Engineering University College of Engineering Tindivanam Tindivanam 604001 India Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia Computer Science Department Community College King Saud University Riyadh 11437 Saudi Arabia

Smart cities, as a typical application in the field of the Internet of Things, can combine cloud computing to realize the intelligent control of objects and process massive data. While cloud computing brings convenience to smart city services, a serious problem is ensuring that confidential data cannot be leaked to malicious adversaries. Considering the security and privacy of data, data owners transmit sensitive data in its encrypted form to cloud server, which seriously hinders the improvements of potential utilization and efficient sharing. Public key searchable encryption ensures that users can securely retrieve the encrypted data without decryption. However, most existing schemes cannot resist keyword guessing attacks or the size of trapdoors linearly increases with the number of data owners. In this work, by utilizing certificateless encryption and proxy re-encryption, we design an authenticated searchable encryption scheme with constant trapdoors. The designed scheme preserves the privacy of index ciphertexts and keyword trapdoors, and can resist keyword guessing attacks. In addition, data users can generate and upload trapdoors with lower computation and communication overheads. We show that the proposed scheme is suitable for smart city implementations and applications by experimentally evaluating its performance.

关键词： Smart city Data retrieval Privacy protection Certificateless cryptography

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Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic

arXiv

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arXiv 2021年

作者： Salari, Ali Chatelain, Yohan Kiar, Gregory Glatard, Tristan Department of Computer Science and Software Engineering Concordia University MontréalQC Canada Center for the Developing Brain Child Mind Institute New YorkNY United States

Operating system (OS) updates introduce numerical perturbations that impact the reproducibility of computational pipelines. In neuroimaging, this has important practical implications on the validity of computational results, particularly when obtained in systems such as high-performance computing clusters where the experimenter does not control software updates. We present a framework to reproduce the variability induced by OS updates in controlled conditions. We hypothesize that OS updates impact computational pipelines mainly through numerical perturbations originating in mathematical libraries, which we simulate using Monte-Carlo arithmetic in a framework called "fuzzy lib-math" (FL). We applied this methodology to pre-processing pipelines of the Human Connectome Project, a flagship open-data project in neuroimaging. We found that FL-perturbed pipelines accurately reproduce the variability induced by OS updates and that this similarity is only mildly dependent on simulation parameters. Importantly, we also found between-subject differences were preserved in both cases, though the between-run variability was of comparable magnitude for both FL and OS perturbations. We found the numerical precision in the HCP preprocessed images to be relatively low, with less than 8 significant bits among the 24 available, which motivates further investigation of the numerical stability of components in the tested pipeline. Overall, our results establish that FL accurately simulates results variability due to OS updates, and is a practical framework to quantify numerical uncertainty in neuroimaging. © 2021, CC BY.

关键词： Pipelines

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Experimental Simulation of Larger Quantum Circuits with Fewer Superconducting Qubits

引用

Physical Review Letters 2023年第11期130卷 110601-110601页

作者： Chong Ying Bin Cheng Youwei Zhao He-Liang Huang Yu-Ning Zhang Ming Gong Yulin Wu Shiyu Wang Futian Liang Jin Lin Yu Xu Hui Deng Hao Rong Cheng-Zhi Peng Man-Hong Yung Xiaobo Zhu Jian-Wei Pan Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences University of Science and Technology of China Hefei 230026 China Shanghai Research Center for Quantum Science and CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Hefei National Laboratory University of Science and Technology of China Hefei 230088 China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Department of Physics Southern University of Science and Technology Shenzhen 518055 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou Henan 450000 China QuTech Delft University of Technology P.O. Box 5046 2600 GA Delft Netherlands Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China

Although near-term quantum computing devices are still limited by the quantity and quality of qubits in the so-called NISQ era, quantum computational advantage has been experimentally demonstrated. Moreover, hybrid architectures of quantum and classical computing have become the main paradigm for exhibiting NISQ applications, where low-depth quantum circuits are repeatedly applied. In order to further scale up the problem size solvable by the NISQ devices, it is also possible to reduce the number of physical qubits by “cutting” the quantum circuit into different pieces. In this work, we experimentally demonstrated a circuit-cutting method for simulating quantum circuits involving many logical qubits, using only a few physical superconducting qubits. By exploiting the symmetry of linear-cluster states, we can estimate the effectiveness of circuit-cutting for simulating up to 33-qubit linear-cluster states, using at most 4 physical qubits for each subcircuit. Specifically, for the 12-qubit linear-cluster state, we found that the experimental fidelity bound can reach as much as 0.734, which is about 19% higher than a direct implementation on the same 12-qubit superconducting processor. Our results indicate that circuit-cutting represents a feasible approach of simulating quantum circuits using much fewer qubits, while achieving a much higher circuit fidelity.

关键词： Quantum algorithms Quantum computation Quantum information with solid state qubits Quantum simulation Superconducting qubits

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Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

arXiv

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arXiv 2022年

作者： Jagatheesaperumal, Senthil Kumar Pham, Quoc-Viet Ruby, Rukhsana Yang, Zhaohui Xu, Chunmei Zhang, Zhaoyang Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamil Nadu Sivakasi India The Korean Southeast Center for the 4th Industrial Revolution Leader Education Pusan National University Busan46241 Korea Republic of College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310007 China Zhejiang Lab Hangzhou31121 China The School of Information Science and Engineering Southeast University China

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases. © 2022, CC BY.

关键词： Internet of things

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

引用

作者： Lianyong Qi Fan Wang Xiaolong Xu Wanchun Dou Xuyun Zhang Mohammad R.Khosravi Xiaokang Zhou School of Computer Science Qufu Normal University School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Department of Computing Macquarie University Department of Computer Engineering Persian Gulf University Department of Electrical and Electronic Engineering Shiraz University of Technology Faculty of Data Science Shiga University

With the continuous development of Io T, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies,which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacyaware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy.

关键词： Traffic flow prediction Multi-party sensors Privacy Locality-Sensitive Hashing

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Robust Fault Detection of Nonlinear Uncertain Systems with Event-Triggered Communication ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13694-13699页

作者： Dong Zhao Marios M. Polycarpou KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus CY-1678 Nicosia Cyprus

The robust fault detection problem for a class of nonlinear uncertain systems with event-triggered measurement communication is addressed in this study. The proposed event-triggered robust fault detection scheme has two steps. First, with the event-triggered measurement, an adaptive approximator is proposed to learn the unknown modeling uncertainty online. Second, after the learning procedure, an event-triggered residual generator is designed by integrating the adaptive approximator with the residual signal for fault detection. The adaptive threshold for fault detection decision is derived by taking the event-triggered scheme into consideration. The performance of the event-triggered fault detection system is rigorously analyzed by characterizing the event-triggered sampling error, including the stability of the adaptive approximation and the fault detectability.

关键词： adaptive approximation fault detection nonlinear systems event-triggered scheme

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ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data

arXiv

引用

arXiv 2022年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Wu, Peixi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China The School of Dentistry Shenzhen University Shenzhen China

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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An access control scheme in IoT-enabled Smart-Grid systems using blockchain and PUF

引用

Internet of Things (Netherlands) 2023年 22卷

作者： Zahoor, Amina Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Ayub, Muhammad Faizan Conti, Mauro Das, Ashok Kumar Department of Computer Science COMSATS University Islamabad Sahiwal Campus 57000 Pakistan Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Software Engineering The University of Lahore Lahore 54590 Pakistan Department of Computer Science University of Sahiwal Sahiwal 57000 Pakistan Department of Mathematics University of Padua Padua 35131 Italy Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India

IoT-enabled Smart Grid (IoT-SG) is an emerging paradigm that enables the bi-direction communication of IoT devices and hardware to efficiently collect and transmit the consumer's information over the Internet. However, the underlying open communication network and resource-constrained capabilities of devices invite manifold challenges and threats in terms of security and privacy. To alleviate such issues, we designed a private blockchain-based access control protocol for IoT-SG using Physically Unclonable Function (PUF). Our protocol allows the Service Provider (SP) and smart meters to transfer the data in an efficient and secure manner. The participating SPs form the Peer-to-Peer (P2P) network, and each peer node is responsible for securely creating the blocks from the gathered data. Thereafter, all the peer nodes employ a voting-based consensus mechanism to verify and add the recently created block into the blockchain network. We have rigorously validated the security of our protocol under the Random or Real (RoR) model. The testbed results and security feature analysis show that our protocol is more efficient than competing ones while it also provides significant security properties. © 2023 Elsevier B.V.

关键词： Authentication Blockchain Internet of Things (IoT) Key agreement Security Smart power grids

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Image-based Lung Analysis in the Context of Digital Pathology: a Brief Review

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Procedia computer Science 2024年 239卷 2168-2175页

作者： Somayeh Shahrabadi João Carias Emanuel Peres Luís G. Magalhães Miguel A. Guevara López Luís Bastião Silva Telmo Adão CCG/ZGDV: Center for Computer Graphics Campus de Azurém Edifício 14 4800-058 Guimarães Portugal ALGORITMI Research Centre University of Minho Guimarães Portugal Department of Engineering School of Sciences and Technology University of Trás-os-Montes e Alto Douro 5000-801 Vila Real Portugal Instituto Politécnico de Setúbal Escola Superior de Tecnologia de Setúbal 2914-508 Setúbal Portugal BMD Software Aveiro Portugal

Lung cancer is the 2 nd most diagnosed cancer worldwide. The corresponding histopathological analysis, being both costly and time-consuming, demands the commitment of skilled professionals who, while engrossed in this task, experience constraints on their ability to attend to other crucial responsibilities. Moreover, as it is a human-driven process, mistakes may lead to incorrect diagnosis and treatment. Given the disease frequency and mortality, automated diagnostic systems, using Artificial Intelligence (AI), represent valuable improvements in diagnostic timing and overall performance. Recently, Deep Learning (DL) has been widely used for extracting features from histopathologic images approaching more accurate and expeditious analysis. With this line of research in mind, a brief review of recent technical/scientific works within the scope of lung Digital Pathology (DP) image analysis is provided in this paper, covering different computer vision tasks including classification, segmentation, and detection. Furthermore, available datasets and open-source annotation tools capable of providing support to the aforementioned DP-related tasks are also overviewed. Afterward, a summary table and a discussion around the reviewed approaches is provided, consolidating critical information such as technique/DL architecture, involved datasets, metrics, etc. From this study, it was observed that the ARA-CNN technique achieved the highest Area Under the Curve (AUC) ranging from 0.72 to 0.99 for classification. On the other hand, the multimodal-based approach, with an AUC of 0.95, performed better for the segmentation task. As for the detection task, the BCNN approach stood out, achieving a high AUC of 0.988. This review work aims to provide a comprehensive overview of recent advancements in lung DP image analysis and serves as a foundation for future research in this critical area.

关键词： Artificial Intelligence Machine Learning computer Vision Digital Pathology Image Diagnosis Whole-slide Imaging Lung Cancer

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Unscented stochastic model predictive perimeter control of uncertain two-regions urban traffic

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IFAC-PapersOnLine 2020年第2期53卷 14930-14935页

作者： Ehsan Harati Stelios Timotheou Christos Panayiotou University of Cyprus KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia 1678

In this article, Stochastic Model Predictive Control (SMPC) is employed for optimal perimeter control of traffic flow with uncertain Macroscopic Fundamental Diagram (MFD), traffic accumulation and traffic demand of two regions. Two regions urban traffic networks are described through the MFD. The MFD is a fundamental relation between average flow (production) and density (accumulation) in urban regions. Although the MFD is often assumed as a simple deterministic curve, possible heterogeneity in urban regions results in large scattering of the MFD pattern. Traffic accumulation is considered uncertain due to limited sources of measurements. Moreover, traffic demand is based on the stochastic nature of drivers. The stochastic uncertainty is modeled through appropriate probability distribution functions for MFD, traffic accumulation and demand. Simulation results show the superiority of the proposed method compared to deterministic MPC in the presence of model mismatch.

关键词： Macroscopic fundamental diagram Predictive control Uncertainty Unscented transform Traffic control Probabilistic models

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