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Secure and Efficient Data Storage and Sharing Scheme Based on Double Blockchain

Computers, Materials & Continua 2021年第1期66卷 499-515页

作者： Lejun Zhang Minghui Peng Weizheng Wang Yansen Su Shuna Cui Seokhoon Kim College of Information Engineering Yangzhou UniversityYangzhou225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou362000China Division of Computer Science University of AizuAizu–Wakamatsu9658580Japan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Medical College of Yangzhou University Yangzhou225001China Department of Gynecology and Obstetrics Affiliated Hospital of Yangzhou UniversityYangzhouChina Department of Computer Software Engineering Soonchunhyang UniversityAsanKorea

In the digital era,electronic medical record(EMR)has been a major way for hospitals to store patients’medical *** traditional centralized medical system and semi-trusted cloud storage are difficult to achieve dynamic balance between privacy protection and data *** storage capacity of blockchain is limited and single blockchain schemes have poor scalability and low *** address these issues,we propose a secure and efficient medical data storage and sharing scheme based on double *** our scheme,we encrypt the original EMR and store it in the *** storage blockchain stores the index of the complete EMR,and the shared blockchain stores the index of the shared part of the *** with different attributes can make requests to different blockchains to share different parts according to their own *** experiments,it was found that cloud storage combined with blockchain not only solved the problem of limited storage capacity of blockchain,but also greatly reduced the risk of leakage of the original *** Extraction Signature(CES)combined with the double blockchain technology realized the separation of the privacy part and the shared part of the original *** symmetric encryption technology combined with Ciphertext-Policy Attribute-Based Encryption(CP–ABE)not only ensures the safe storage of data in the cloud,but also achieves the consistency and convenience of data update,avoiding redundant backup of *** analysis and performance analysis verified the feasibility and effectiveness of our scheme.

关键词： Cloud storage blockchain electronic medical records access control data sharing privacy

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers 6

Performance and energy-aware bi-objective tasks scheduling f...

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6th Information Systems International Conference, ISICO 2021

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021 The Authors. Published by Elsevier B.V.

关键词： Quality of service

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Secure and Privacy-Preserving Lightweight Blockchain for Energy Trading 8

Secure and Privacy-Preserving Lightweight Blockchain for Ene...

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8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Materwala, Huned Ismail, Leila College of Information Technology United Arab Emirates University Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates

ISBN: (纸本)9781665425742

Renewable energy is gaining wide attention to address the issues, such as depleting resources and environmental degradation, linked with the current non-renewable energy. However, storing renewable energy is challenging. Consequently, energy trading is required to transfer the surplus amount of renewable energy from the prosumers to the consumers. The current centralized energy trading system is less secure and involves large transmission losses and high overheads. The decentralization, replication, immutability, and transparency characteristics of blockchain show a promising potential to address the inherent issues of centralized energy trading. However, the existing blockchain-based energy trading suffers from the drawbacks such as limited scalability, high energy loss, and low transaction throughput. In this paper, we propose a secure and privacy-preserving lightweight blockchain for energy trading that reduces the computational and communication overhead compared to the bitcoin network. This is by clustering the prosumers and perform the energy trading through a cluster head. Our experimental results show the potential of our architecture. © 2021 IEEE.

关键词： Scalability

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

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Procedia Computer Science 2022年 197卷 238-246页

作者： Huned Materwala Leila Ismail Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users’ tasks. The pervasive adoption of cloud computing in smart cities’ services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.

关键词： Autonomous agents cloud computing energy-efficiency evolutionary algorithm genetic algorithm quality of service multi-objective optimization performance

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Towards Privacy-Preserving Aggregated Prediction from SPDZ 3rd

Towards Privacy-Preserving Aggregated Prediction from SPDZ

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Zhao, Qi Zhang, Bo Yan, Anli Jing, Shan Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering Jinan China

ISBN: (纸本)9783030622220

Machine learning models trained on data collected from multiple parties can offer prediction services to clients. However, it raises privacy concerns for both model owners and clients. The models may disclose the details of the training data inadvertently by querying and the clients’ private input may be obtained by service providers. In this work, a privacy-preserving aggregated prediction framework is proposed, which combines two privacy-preserving techniques, i.e., Differential Privacy and Secret Sharing, to ensure privacy. Specifically, individual parties first train local models that meet differential privacy. Then two non-colluding servers collect the shares of multi-party trained models and clients’ inputs independently and provide online prediction. Finally, the clients reconstruct prediction from servers and aggregate them into the final prediction. It is worth mentioning that during prediction phase, no one can obtain the private information of others. We evaluate the performance of our framework on MNIST dataset. The experimental results show that the framework can strike a balance between utility and privacy. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Improving Critical Node Detection Using Neural Network-based Initialization in a Genetic Algorithm

arXiv

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arXiv 2024年

作者： Liu, Chanjuan Ge, Shike Chen, Zhihan Pei, Wenbin Zhu, Enqiang Mei, Yi Ishibuchi, Hisao The School of Computer Science and Technology Dalian University of Technology 116024 China The Institute of Software Chinese Academy of Science Beijing100190 China The Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China The School of Engineering and Computer Science Victoria University of Wellington Wellington6012 New Zealand The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The Critical Node Problem (CNP) is concerned with identifying the critical nodes in a complex network. These nodes play a significant role in maintaining the connectivity of the network, and removing them can negatively impact network performance. CNP has been studied extensively due to its numerous real-world applications. Among the different versions of CNP, CNP-1a has gained the most popularity. The primary objective of CNP-1a is to minimize the pair-wise connectivity in the remaining network after deleting a limited number of nodes from a network. Due to the NP-hard nature of CNP-1a, many heuristic/metaheuristic algorithms have been proposed to solve this problem. However, most existing algorithms start with a random initialization, leading to a high cost of obtaining an optimal solution. To improve the efficiency of solving CNP-1a, a knowledge-guided genetic algorithm named K2GA has been proposed. Unlike the standard genetic algorithm framework, K2GA has two main components: a pretrained neural network to obtain prior knowledge on possible critical nodes, and a hybrid genetic algorithm with local search for finding an optimal set of critical nodes based on the knowledge given by the trained neural network. The local search process utilizes a cut node-based greedy strategy. The effectiveness of the proposed knowledge-guided genetic algorithm is verified by experiments on 26 real-world instances of complex networks. Experimental results show that K2GA outperforms the state-of-the-art algorithms regarding the best, median, and average objective values, and improves the best upper bounds on the best objective values for eight real-world instances. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Leveraging the Dual Capabilities of LLM: LLM-Enhanced Text Mapping Model for Personality Detection 39

Leveraging the Dual Capabilities of LLM: LLM-Enhanced Text M...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bi, Weihong Kou, Feifei Shi, Lei Li, Yawen Li, Haisheng Chen, Jinpeng Xu, Mingying School of Computer Science National Pilot School of Software Engineering BUPT Beijing100876 China Key Laboratory of Trustworthy Distributed Computing and Service BUPT Ministry of Education Beijing100876 China State Key Laboratory of Media Convergence and Communication CUC Beijing100024 China State Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPU Taicang215400 China School of Economics and Management BUPT Beijing100876 China Beijing Technology and Business University Beijing100048 China Xiangjiang Laboratory Changsha410205 China North China University of Technology Beijing100144 China

ISBN: (纸本)157735897X

Personality detection aims to deduce a user’s personality from their published posts. The goal of this task is to map posts to specific personality types. Existing methods encode post information to obtain user vectors, which are then mapped to personality labels. However, existing methods face two main issues: first, only using small models makes it hard to accurately extract semantic features from multiple long documents. Second, the relationship between user vectors and personality labels is not fully considered. To address the issue of poor user representation, we utilize the text embedding capabilities of LLM. To solve the problem of insufficient consideration of the relationship between user vectors and personality labels, we leverage the text generation capabilities of LLM. Therefore, we propose the LLM-Enhanced Text Mapping Model (ETM) for Personality Detection. The model applies LLM’s text embedding capability to enhance user vector representations. Additionally, it uses LLM’s text generation capability to create multi-perspective interpretations of the labels, which are then used within a contrastive learning framework to strengthen the mapping of these vectors to personality labels. Experimental results show that our model achieves state-of-the-art performance on benchmark datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mapping

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Speech Emotion Recognition Based on Three-Channel Feature Fusion of CNN and BiLSTM 2020

Speech Emotion Recognition Based on Three-Channel Feature Fu...

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4th International Conference on Innovation in Artificial Intelligence, ICIAI 2020

作者： Huang, Lilong Dong, Jing Zhou, Dongsheng Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781450376587

It is important for human-computer natural interaction that the computer can quickly and effectively identify the interactor's emotional state. We proposed a three-channel feature fusion algorithm based on Convolutional Neural Network and Bidirectional Long Short-Term Memory Network for speech emotion recognition. Mel-Frequency Cepstral Coefficients and its first and second-order difference features were input into three channels and trained respectively for exploiting the emotional information contained in the dynamic features. By splicing and fusing the features obtained by three channels, the recognition rates of proposed algorithm reached 96.562% and 92.34% on CASIA corpus with six emotional states and on EMO-DB corpus with seven emotional states respectively. © 2020 ACM.

关键词： Long short-term memory

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Multi-granularity textual adversarial attack with behavior cloning

arXiv

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arXiv 2021年

作者： Chen, Yangyi Su, Jin Wei, Wei Cognitive Computing and Intelligent Information Processing Laboratory School of Computer Science and Technology Huazhong University of Science Tehchnology School of Software Engineering Huazhong University of Science Tehchnology

Recently, the textual adversarial attack models become increasingly popular due to their successful in estimating the robustness of NLP models. However, existing works have obvious deficiencies. (1) They usually consider only a single granularity of modification strategies (e.g. word-level or sentence-level), which is insufficient to explore the holistic textual space for generation;(2) They need to query victim models hundreds of times to make a successful attack, which is highly inefficient in practice. To address such problems, in this paper we propose MAYA, a MultigrAnularitY Attack model to effectively generate high-quality adversarial samples with fewer queries to victim models. Furthermore, we propose a reinforcement-learning based method to train a multi-granularity attack agent through behavior cloning with the expert knowledge from our MAYA algorithm to further reduce the query times. Additionally, we also adapt the agent to attack black-box models that only output labels without confidence scores. We conduct comprehensive experiments to evaluate our attack models by attacking BiLSTM, BERT and RoBERTa in two different black-box attack settings and three benchmark datasets. Experimental results show that our models achieve overall better attacking performance and produce more fluent and grammatical adversarial samples compared to baseline models. Besides, our adversarial attack agent significantly reduces the query times in both attack settings. Our codes are released at https://***/Yangyi-Chen/MAYA. © 2021, CC BY-NC-SA.

关键词： Cloning

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Missing Data Recovery for Human Mocap Data Based on A-LSTM and LS Constraint 5

Missing Data Recovery for Human Mocap Data Based on A-LSTM a...

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5th IEEE International Conference on Signal and Image Processing, ICSIP 2020

作者： Ji, Lu Liu, Rui Zhou, Dongsheng Zhang, Qiang Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781728168968

Human mocap (motion capture) data has been widely used in many fields. However, influenced by the occlusion and falling, missing of markers is inevitable in optical motion capture system. According to the temporal and spatial characteristics of mocap data, a missing data recovery method based on attention-based LSTM network (A-LSTM) and least-squares (LS) constraint is proposed in this paper. First, the LSTM is chosen as the basic network to estimate the position of the missing markers, in which an attention mechanism is introduced to learn the temporal continuity of human mocap data. Then, a space affine transformation based on least-squares fitting is adopted as the spatial constraint to refine the estimated positions, after which the restored error can be reduced significantly. Experiments show that the proposed A-LSTM+LS method is robust to the length of missing gap and the number of missing markers. Under the same conditions, our method achieves smaller restore error than the state of the art. © 2020 IEEE.

关键词： Least squares approximations

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