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Privacy-Preserving Electricity Theft Detection Based on Blockchain

IEEE Transactions on Smart Grid 2023年第5期14卷 4047-4059页

作者： Zhao, Zhiqiang Liu, Yining Zeng, Zhixin Chen, Zhixiong Zhou, Huiyu Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Fujian Key Laboratory of Financial Information Processing The Key Laboratory of Applied Mathematics of Fujian Province University Putian University Putian351100 China University of Leicester School of Computing and Mathematical Sciences LeicesterLE1 7RH United Kingdom

In most electricity theft detection schemes, consumers' power consumption data is directly input into the detection center. Although it is valid in detecting the theft of consumers, the privacy of all consumers is at risk unless the detection center is assumed to be trusted. In fact, it is impractical. Moreover, existing schemes may result in some security problems, such as the collusion attack due to the presence of a trusted third party, and malicious data tampering caused by the system operator (SO) being attacked. Aiming at the problems above, we propose a blockchain-based privacy-preserving electricity theft detection scheme without a third party. Specifically, the proposed scheme uses an improved functional encryption scheme to enable electricity theft detection and load monitoring while preserving consumers' privacy;distributed storage of consumers' data with blockchain to resolve security problems such as data tampering, etc. Meanwhile, we build a long short-term memory network (LSTM) model to perform higher accuracy for electricity theft detection. The proposed scheme is evaluated in a real environment, and the results show that it is more accurate in electricity theft detection within acceptable communication and computational overhead. Our system analysis demonstrates that the proposed scheme can resist various security attacks and preserve consumers' privacy. © 2010-2012 IEEE.

关键词： Long short-term memory

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A Platform for Automated Solar Data Analysis Using Machine Learning 9

A Platform for Automated Solar Data Analysis Using Machine L...

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9th International Conference on Social Networks Analysis, Management and security, SNAMS 2022

作者： Younis, Ola Tashtoush, Yahya Alomari, Mohammad Darwish, Omar Department of Computer Science University of Liverpool Liverpool United Kingdom Computer Science Department Jordan University of Science and Technology Jordan Applied Science Research Center Applied Science Private University Jordan Information Security and Applied Computing Eastern Michigan University Ypsilanti United States

ISBN: (纸本)9798350320480

This paper presents a computer platform for the automated analysis of associations among different solar events and activities. This computer tool enables the advanced learning by implementing many associations' algorithms to analyze years of solar catalogues data and to study the associations among solar flares, eruptive filaments per prominences and Coronal Mass Ejections (CMEs). The aim is to combine all solar data catalogues in one dynamic space weather database that can be easily used in the analysis of solar activities and features. The computer tool identifies patterns of associations and provides numerical representations that can be used as inputs to the machine learning algorithms to provide computerized learning rules that can be developed in the future within the context of a real-time prediction system. © 2022 IEEE.

关键词： Data mining

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Emerging Research in the security of Modern and Autonomous Vehicles

Emerging Research in the Security of Modern and Autonomous V...

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IEEE International Conference on Electro information Technology

作者： Samir Tout Munther Abualkibash Pooja Patil School of Information Security & Applied Computing Department of Computer Science Eastern Michigan University

The last decade has witnessed considerable advancements in automating modern "connected" and Autonomous Vehicles (AVs), which are becoming an integral part of the modern transportation systems. Such advancements have been attributed to major strides in Machine Learning, Artificial Intelligence and processing technology. More importantly, the complexity of such systems and their lack of built-in security have brought to the spotlight the need for extensive research to understand the associated risks and propose mitigations for them. In this paper we briefly discuss the security aspects of modern vehicles and provide a concise literature review of the work that has been done in this domain. We focus on specific areas that are of interest to us and conclude by providing a plan for future related endeavors.

关键词： Vehicle Autonomous security Networks Hacking

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Epileptic Spike Detection Based on Deep Learning 23

Epileptic Spike Detection Based on Deep Learning

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9th International Conference on computing and Artificial Intelligence, ICCAI 2023

作者： Duan, Lijuan Cui, Yuanqi Wang, Yao Wang, Zeyu Cao, Min Qiao, Yuanhua Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China College of Applied Science Beijing University of Technology University Beijing China

ISBN: (纸本)9781450399029

Epilepsy is a chronic disease with high prevalence and high disease burden. Electroencephalogram (EEG) is one of the most important tools for diagnosing epilepsy. Detection of epileptic spikes from EEG can effectively aid the diagnosis of epilepsy but it is also a time-consuming and laborious process for neurologists, which can only be addressed by automatic spike detection methods. However, existing approaches are limited by the simplistic nature of conventional machine learning algorithms. The work researched about how human experts identify spikes and over-parameterized the process into a deep neural network named temporal-frequential and multi-order difference fusion (TeFreDiA) model which can accurately detect epileptic *** model was evaluated on data collected from 7 subjects with ten-fold cross-validation. Given an average accuracy and sensitivity of 95.64% and 96.89%, the model can be utilized to aid the diagnosis of epilepsy. © 2023 Copyright held by the owner/author(s).

关键词： Electroencephalography

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A new attack on three variants of the RSA cryptosystem 21st

A new attack on three variants of the RSA cryptosystem

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21st Australasian Conference on information security and Privacy, ACISP 2016

作者： Bunder, Martin Nitaj, Abderrahmane Susilo, Willy Tonien, Joseph School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia Département de Mathématiques Université de Caen Caen France School of Computing and Information Technology Centre for Computer and Information Security Research University of Wollongong Wollongong Australia

ISBN: (纸本)9783319403663

In 1995, Kuwakado, Koyama and Tsuruoka presented a new RSA-type scheme based on singular cubic curves y2 = x3 + bx2 (mod N) where N = pq is an RSA modulus. Then, in 2002, Elkamchouchi, Elshenawy and Shaban introduced an extension of the RSA scheme to the field of Gaussian integers using a modulus N = PQ where P and Q are Gaussian primes such that p = |P| and q = |Q| are ordinary primes. Later, in 2007, Castagnos proposed a scheme over quadratic field quotients with an RSA modulus N = pq. In the three schemes, the public exponent e is an integer satisfying the key equation ed - k (p2 - 1) (q2 - 1) = 1. In this paper, we apply the continued fraction method to launch an attack on the three schemes when the private exponent d is sufficiently small. Our attack can be considered as an extension of the famous Wiener attack on the RSA. © Springer International Publishing Switzerland 2016.

关键词： Artificial intelligence

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A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning

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Intelligent Automation & Soft computing 2023年第8期37卷 2037-2057页

作者： Khalid M.O.Nahar Ammar Almomani Nahlah Shatnawi Mohammad Alauthman Department of Computer Sciences Faculty of Information Technology and Computer SciencesYarmouk University-Irbid21163Jordan School of Computing Skyline University CollegeSharjahP.O.Box 1797United Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken *** proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition *** image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning *** results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of *** offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute *** work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.

关键词： Sign language deep learning transfer learning machine learning automatic translation of sign language natural language processing Arabic sign language

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A Communication-efficient Conjunctive Query Scheme under Local Differential Privacy

A Communication-efficient Conjunctive Query Scheme under Loc...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Ellen Z. Guan, Yunguo Lu, Rongxing Zhang, Harry University of New Brunswick Faculty of Computer Science FrederictonE3B5A3 Canada Eastern Michigan University School of Information Security and Applied Computing YpsilantiMI48197 United States

ISBN: (纸本)9798350351255

Crowdsourcing has become a widely used method for data collection and analysis, yet its privacy remains a challenge. In this paper, we present a new efficient and privacy-preserving conjunctive query scheme for crowdsourcing scenarios. The scheme employs the Local Differential Privacy (LDP) technique to ensure both query privacy and high communication efficiency. Specifically, when an aggregator launches a conjunctive query to a set of crowdsourcing users, the query condition will not be leaked. To respond the query, each user just needs to return one bit back to the aggregator. By integrating prefix encoding technique, our proposed scheme can also efficiently support conjunctive queries with one range query condition. Detailed security analysis shows our proposed scheme can achieve desirable security requirements. In addition, performance evaluations also indicate its efficiency. Furthermore, extensive experiments demonstrate our proposed scheme can achieve high accuracy while ensuring ϵ-LDP. © 2024 IEEE.

关键词： Differential privacy

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

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

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2020 International Conference on Connected and Autonomous Driving, MetroCAD 2020

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

ISBN: (纸本)9781728160597

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. © 2020 IEEE.

关键词： Synchronization

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Comparative Analysis of Sequential and Parallel computing for Object Detection Using Deep Learning Model 24

Comparative Analysis of Sequential and Parallel Computing fo...

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24th International Arab Conference on information Technology, ACIT 2023

作者： Amsaad, Fathi Al-Eidi, Shorouq Darwish, Omar Computer Science and Engineering Wright State University Ohio United States Tafila Technical University Computer Science Department Tafila Jordan Information Security and Applied Computing Eastern Michigan University Michigan United States

ISBN: (纸本)9798350384307

Recently, parallel and distributed computing has become indispensable in various application domains, such as cloud computing and extensive data analysis, to meet the growing demands for efficiency and reliability. In this study, we compared sequential and parallel computing techniques applied to a deep learning model for object detection. By leveraging multiple evaluation metrics, such as F1 score, execution time, and accuracy, we meticulously evaluated the model's performance. Our analysis revealed that parallel execution considerably decreased the training time of model, resulting in an average time savings of 40 % while maintaining the training process's accuracy and quality. These finding have significant implications for real world applications, as they demonstrate tremendously the potential for expedited deep learning model training without compromising accuracy. This can tremendously benefit medical imaging and realtime video analysis, where accuracy and efficiency are paramount. © 2023 IEEE.

关键词： Deep neural networks Parallel computing Sequential computing

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Hierarchical certification of nonclassical network correlations

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Physical Review A 2024年第2期110卷 022617页

作者： Ming-Xing Luo Xue Yang Alejandro Pozas-Kerstjens Information Security and National Computing Grid Laboratory CAS Center for Excellence in Quantum Information and Quantum Physics Hefei 230026 China Group of Applied Physics 8200 Schaffhausen Switzerland (

With the increased availability of quantum technological devices, it becomes more important to have tools to guarantee their correct nonclassical behavior. This is especially important for quantum networks, which constitute the platforms where multipartite cryptographic protocols will be implemented, and where guarantees of nonclassicality translate into security proofs. We derive linear and nonlinear Bell-like inequalities for networks, whose violation certifies the absence of a minimum number of classical sources in them. We do so, first, without assuming that nature is ultimately governed by quantum mechanics, providing a hierarchy interpolating between network nonlocality and full network nonlocality. Second we insert this assumption, which leads to results more amenable to certification in experiments.

关键词： Nonlocality Quantum communication, protocols & technology Quantum correlations in quantum information Quantum correlations, foundations & formalism Quantum information processing Quantum information theory Quantum networks

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