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IEEE International Conference on Communications (ICC)

作者： Ellen Z. Zhang Yunguo Guan Yantao Yu Rongxing Lu Harry Zhang Faculty of Computer Science University of New Brunswick Fredericton Canada School of Information Security and Applied Computing Eastern Michigan University Ypsilanti MI USA College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Crowdsourcing has received considerable attention in recent years; however, privacy in crowdsourcing remains a challenge. In this paper, we present a privacy-preserving range sum query scheme under Local Differential Privacy (LDP) that not only enhances accuracy but also guarantees privacy in crowdsourcing applications. Specifically, our proposed scheme employs keyed hash, prefix encoding, and garbled bloom filter techniques to convert a large query range into a small domain, independent of the range length, thus improving accuracy. For the query response, the Optimal Unary Encoding (OUE) technique is applied to achieve ε-LDP. security analysis shows that our proposed scheme can achieve the desirable privacy requirement for users' private items and the server's query range. In addition, performance evaluations also confirm the efficiency of our scheme in terms of computational costs and communication overhead. Furthermore, extensive experiments validate that our proposed scheme outperforms a potential strawman solution in terms of accuracy.

关键词： Crowdsourcing Performance evaluation Privacy Differential privacy Accuracy Transforms Encoding

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Efficient Privacy-Preserving Multi-Location Task Allocation in Fog-Assisted Vehicular Crowdsourcing

Efficient Privacy-Preserving Multi-Location Task Allocation ...

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IEEE International Conference on Communications (ICC)

作者： Yantao Yu Yunguo Guan Xiaoping Xue Jingxiao Ma Ellen Z. Zhang Rongxing Lu College of Electronic and Information Engineering Tongji University Shanghai China Faculty of Computer Science University of New Brunswick Frederiction NB Canada School of Information Security and Applied Computing Eastern Michigan University Ypsilanti MI USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Multi-location task allocation is one of the most crucial issues in vehicular crowdsourcing (VCS). To ensure service quality, the VCS service provider prefers to assign multi-location tasks to the workers whose future trajectories have high spatial proximity with the task locations. However, this process requires workers and task owners to upload their precise locations to a not-fully-trusted service provider, thereby raising location privacy concerns. Although several privacy-preserving trajectory similarity evaluation schemes have been proposed, they either fail to match the multi-location task allocation scenario, or incur nontrivial computational costs due to homomorphic encryption. To address these challenges, we propose a novel efficient privacy-preserving multi-location task allocation scheme in fog-assisted VCS. Specifically, we design a lightweight secure Euclidean distance computation protocol based on arithmetic secret sharing (ASS), which can compute Euclidean distance without revealing the two input locations. Then, based on this protocol, we build our scheme that supports multi-location task allocation based on Hausdorff semi-distance (HSD). Our security analysis demonstrates the location privacy preservation of our scheme, and the experiment results on a real dataset also validate the efficiency of our scheme.

关键词： Crowdsourcing Privacy Data privacy Protocols Euclidean distance Trajectory Computational efficiency

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Efficient Privacy-Preserving Edge-based Dynamic Aggregation Query Over Crowdsensed Data

Efficient Privacy-Preserving Edge-based Dynamic Aggregation ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yantao Yu Yunguo Guan Xiaoping Xue Rongxing Lu College of Electronic and Information Engineering Tongji University Shanghai China School of Information Security and Applied Computing Eastern Michigan University Ypsilanti MI USA Faculty of Computer Science University of New Brunswick Frederiction NB Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Aggregation query, which is one of the most crucial data analysis tools applied to vehicular crowdsensed data, is expected to extract valuable insights and support smart decision-making. Meanwhile, the edge servers are deployed to cope with the escalation of service scale, which however raises privacy concerns about the query requests and reported data. Previously reported privacy-preserving aggregation query schemes are either tailored for static datasets or lack an index structure for deployed queries, which makes them impractical in vehicular crowdsensing (VCS) scenarios characterized by large volumes of real-time data and high data update frequencies. To tackle these challenges, we propose an efficient privacy-preserving edge-based dynamic aggregation query scheme with an encrypted tree-based index. Concretely, we first design two building blocks, namely the balanced spatial encoding quadtree (BSEQtree) and a predicate encryption scheme for membership testing (PEMT), which enable the edge servers to obliviously match data and queries by traversal over the encrypted BSEQtree. Based on the above blocks and arithmetic secret sharing (ASS), we construct our scheme, in which the edge servers can efficiently and securely aggregate newly reported data to related query results. Our security analysis shows the privacy preservation of our scheme, and the experiment results on a real dataset validate the efficiency of our scheme.

关键词： Data privacy Privacy Crowdsensing Data aggregation Real-time systems Cryptography Servers Indexes Vehicle dynamics Testing

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Experimenting with faults, lattices and the DSA

Experimenting with faults, lattices and the DSA

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8th International Workshop on Theory and Practice in Public Key Cryptography, PKC 2005

作者： Naccache, David Nguye˜n, Phong Q. Tunstall, Michael Whelan, Claire Gemplus Card International Applied Research and Security Centre 34 rue Guynemer Issy-les-Moulineaux F-92447 France University of London Information Security Group Royal Holloway Egham Surrey TW20 0EX United Kingdom CNRS/École Normale Supérieure Département d'Informatique 45 rue d'Ulm F-75230 Paris Cedex 05 France Gemplus Card International Applied Research and Security Centre Avenue des Jujubiers La Ciotat F-13705 France School of Computing Dublin City University Ballymun Dublin 9 Ireland

We present an attack on DSA smart-cards which combines physical fault injection and lattice reduction techniques. This seems to be the first (publicly reported) physical experiment allowing to concretely pull-out DSA keys out of smart-cards. We employ a particular type of fault attack known as a glitch attack, which will be used to actively modify the DSA nonce k used for generating the signature: k will be tampered with so that a number of its least significant bytes will flip to zero. Then we apply well-known lattice attacks on El Gamal-type signatures which can recover the private key, given sufficiently many signatures such that a few bits of each corresponding k are known. In practice, when one byte of each k is zeroed, 27 signatures are sufficient to disclose the private key. The more bytes of k we can reset, the fewer signatures will be required. This paper presents the theory, methodology and results of the attack as well as possible countermeasures. © International Association for Cryptologic Research 2005.

关键词： Smart cards

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Image cyberbullying detection and recognition using transfer deep machine learning

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International Journal of Cognitive computing in Engineering 2024年第1期5卷 14-26页

作者： Almomani, Ammar Nahar, Khalid Alauthman, Mohammad Al-Betar, Mohammed Azmi Yaseen, Qussai Gupta, Brij B. School of Computing Skyline University College University City of Sharjah P.O. Box 1797 Sharjah United Arab Emirates I.T. department Al-Huson University College AlBalqa Applied University Irbid Jordan Computer Science Department Faculty of I.T. Yarmouk University Irbid Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Ajman University Ajman United Arab Emirates Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

Cyberbullying detection on social media platforms is increasingly important, necessitating robust computational methods. Current approaches, while promising, have not fully leveraged the combined strengths of deep learning and traditional machine learning for enhanced performance. Moreover, online content complexity requires models that can capture nuanced contexts beyond text, which many current methods lack. This research proposes a novel hybrid approach using deep learning models as feature extractors and machine learning classifiers to improve cyberbullying detection. Extracting features using pre-trained deep learning models like InceptionV3, ResNet50, and VGG16, then feeding them into classifiers like Logistic Regression and Support Vector Machines, enhances understanding of the complex contexts where cyberbullying occurs. Experiments on an image dataset showed that combining deep learning and machine learning achieved higher accuracy than using either approach alone. This novel framework bridges the gap in existing literature and contributes to broader efforts to combat cyberbullying through more nuanced, context-aware detection methods. The hybrid technique demonstrates the potential of blending deep learning's representation learning strengths with machine learning's sample efficiency and interpretability. © 2023

关键词： Learning systems

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Privacy preserving passive DNS

arXiv

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arXiv 2020年

作者： Papadopoulos, Pavlos Pitropakis, Nikolaos Buchanan, William J. Lo, Owen Katsikas, Sokratis School of Computing Edinburgh Napier University Edinburgh United Kingdom Department of Information Security and Communication Technology Norwegian University of Science and Technology Norway Faculty of Pure and Applied Sciences Open University of Cyprus Cyprus

The Domain Name System (DNS) was created to resolve the IP addresses of the web servers to easily remembered names. When it was initially created, security was not a major concern;nowadays, this lack of inherent security and trust has exposed the global DNS infrastructure to malicious actors. The passive DNS data collection process creates a database containing various DNS data elements, some of which are personal and need to be protected to preserve the privacy of the end users. To this end, we propose the use of distributed ledger technology. We use Hyperledger Fabric to create a permissioned blockchain, which only authorized entities can access. The proposed solution supports queries for storing and retrieving data from the blockchain ledger, allowing the use of the passive DNS database for further analysis, e.g. for the identification of malicious domain names. Additionally, it effectively protects the DNS personal data from unauthorized entities, including the administrators that can act as potential malicious insiders, and allows only the data owners to perform queries over these data. We evaluated our proposed solution by creating a proof-of-concept experimental setup that passively collects DNS data from a network and then uses the distributed ledger technology to store the data in an immutable ledger, thus providing a full historical overview of all the records. Copyright © 2020, The Authors. All rights reserved.

关键词： Data acquisition

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HydraView: A Synchronized 360◦-View of Multiple Sensors for Autonomous Vehicles

HydraView: A Synchronized 360◦-View of Multiple Sensors for...

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International Conference on Connected and Autonomous Driving

作者： Luodai Yang Qian Jia Ruijun Wang Jie Cao Weisong Shi College of Engineering and Technology Eastern Michigan University College of Arts and Science Eastern Michigan University Dept.of Computer Science Wayne State University School of Information Security and Applied Computing Eastern Michigan University

ISBN: (数字)9781728160597

ISBN: (纸本)9781728160603

Today's autonomous vehicles will deploy multiple sensors to achieve safe and reliable navigation and precise perception of the environment. Although multiple sensors can be advantageous in terms of providing a robust and complete description of the surrounding area, the synchronization of multi-sensors in real-time processing is extremely important. When data is synchronized, primary functional systems such as localization, perception, planning, and control, will all benefit. In this paper, we proposed a synchronized data illustration and collection method to assist the data processing applications for autonomous driving. Our proposed solution among different sensors can be directly deployed on autonomous vehicles for data integration and environment analysis to support the driving model construction. The experimental results validate that our proposed method can present a 360° synchronized view while providing the capability of real-time scanning with up to 80% reduced latency.

关键词： autonomous vehicles multiple sensor synchronization camera LiDAR transformation matrix Autonomous Vehicles transformation matrix Lidar environmental perception Cameras Sensors multi sensor

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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2024 International Conference on Electrical and Computer Engineering Researches, ICECER 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Abbas, Tahir Ateeq, Karamath College of Arts & Science Applied Science University P.O.Box 5055 Manama Bahrain Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of TIMES Institute Department of Computer Science Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331539733

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems. © 2024 IEEE.

关键词： Prediction models

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$k$kTCQ: Achieving Privacy-Preserving $k$k-Truss Community Queries Over Outsourced Data

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IEEE Transactions on Dependable and Secure computing 2023年第4期21卷 2750-2765页

作者： Yunguo Guan Rongxing Lu Songnian Zhang Yandong Zheng Jun Shao Guiyi Wei School of Information Security and Applied Computing Eastern Michigan University Ypsilanti MI USA Faculty of Computer Science University of New Brunswick Fredericton NB Canada State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Zhejiang Gongshang University Hangzhou China

Community search over graphs, which is believed as a powerful tool for locating subgraphs of closely related vertices, has received considerable attention in recent years, and $k$ -truss is such a popular community search metric to obtain subgraphs in which every edge forms $(k-2)$ triangles. In this paper, we particularly consider $k$ -truss community query services, which will return all $k$ -truss communities containing a given query vertex. As is known, when the size of graph grows, for achieving better performance, it is natural for a service provider to outsource the services to a powerful cloud. However, this stresses the need for privacy-preserving $k$ -truss community query services, as the cloud server is not fully trustable. Over the past years, many schemes focusing on privacy-preserving graph computation have been put forth, but none of them can well support privacy-preserving $k$ -truss community queries. Aiming at this challenge, we first propose a privacy-preserving $k$ -truss community query scheme ( $k$ TCQ) by constructing boolean circuits with homomorphic encryption technique and a table-based index. After that, we also design an efficiency-enhanced version ( $k$ TCQ+) based on a stream cipher scheme to reduce the encrypted index's size and improve the query efficiency. Detailed security analysis shows that both $k$ TCQ and $k$ TCQ+ can well preserve data privacy and access pattern privacy, and extensive experimental results also demonstrate that $k$ TCQ+ can observably reduce the size of encrypted index and the query time by $12\times$ and $5.9\times$ , respectively.

关键词： Indexes Servers Cryptography Privacy Social networking (online) Homomorphic encryption Data privacy

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