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An incentive-compatible rational secret sharing scheme using blockchain and smart contract

science China(Information sciences) 2021年第10期64卷 221-241页

作者： Zerui CHEN Youliang TIAN Changgen PENG State Key Laboratory of Public Big Date College of Computer Science and Technology Guizhou University Institute of Cryptography and Data Security Guizhou University

In the rational cryptographic protocol, the two rational players often fall into the prisoner's dilemma, which is also the case for the rational secret sharing we consider in this paper. First, it is proved that rational secret sharing has a sequential equilibrium in the natural state, so that rational participants will fall into the prisoner's dilemma, resulting in no participants being able to reconstruct the secret ***, to solve this problem, we propose an incentive-compatible rational secret scheme. Specifically, the game tree with imperfect information is constructed to facilitate our analysis and proof, and the strictly dominated strategies are directly eliminated to simplify the game tree. Further more, we describe the motivation of the verifier. Then, we prove that rational players have no motivation to deviate from honest behavior using sequential equilibrium so that rational players can reconstruct the secret correctly. Finally, we complete the simulation using the smart contract and analyze our entire scheme. In addition, the game of our scheme does not need to be repeated multiple times to reach sequential equilibrium, i.e., the game always follows the rational path.

关键词： rational secret sharing game theory sequential equilibrium incentive-compatible smart contract

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Consumption Behavior Analysis of E-commerce Users Based on K-means Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第4期7卷 935-942页

作者： Zhang, Junli Wu, Jingyang Gao, Chenyan Data Science and Big Data Technology Xi’an Eurasia University No.8 Dongyi Road Yanta District Xi’an China Xi’an Eurasia University No.8 Dongyi Road Yanta District Xi’an China New Image International Auckland New Zealand

At present, the pattern of China’s e-commerce retail market has been basically formed, Ali already occupies the first place in the market with more than half of the market share, and Jingdong accounts for less than 50 percent, these two companies together account for more than three-quarters of the overall market share in China. However, our country’s national online shopping market is about to become saturated. Therefore, under the premise of the rapid development of the era of big data, the competition of e-commerce platforms has gradually changed from the competition of the number of users to the competition of refined user management. This paper constructs consumer behavior indicators through feature engineering, and then uses the K-means algorithm to cluster customers into four categories, and name these customers as "iron powder customers","general customers", "develop customers" and "zombie customers" according to their importance to the enterprise from high to low, and analyze the characteristics of different customer groups at the same time, and give corresponding marketing suggestions according to different customer groups. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： K-means clustering

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IPFC: An Attentive Face Completion Network with Identity Preserving 2

IPFC: An Attentive Face Completion Network with Identity Pre...

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2nd International Symposium on Electrical, Electronics and Information Engineering, ISEEIE 2022

作者： Ni, Xin Xie, Haiyong Yang, Yuyan Wang, Shuo Wang, Wenshan Liu, Yifeng University of Science and Technology of China China National Engineering Laboratory for Public Safety by Big Data China China

ISBN: (数字)9781665468749

ISBN: (纸本)9781665468749

Face completion is a challenging task since the facial textures and structures are hard to be captured and generated. Meanwhile, the blank area is usually completed by an average face, which may blur the individual information during the face completion processing. To solve these problems, we propose an Identity-Preserving Face Completion Network (IPFC). Firstly, we design an attentive generative module for achieving the pixel-level spatial relations and use the relations to capture the details of the face images. Then, we employ two discriminators to distinguish face authenticity and individual differences. During the optimization stage, we develop three schemes to minimize the generation error: (1) Full image and eye reconstruction losses are used to provide global and partial supervision, respectively. (2) The face authenticity loss is utilized as a basic restriction to keep the generated data distribution. (3) The individual discriminate loss is designed to capture the identity distribution from the training data without any other extra data. We apply our method on a popular benchmark CelebA-HQ, and it achieves state-of-the-art performances in four metrics. © 2022 IEEE.

关键词： Measurement Training data Benchmark testing Generators Robustness Task analysis Faces

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Task offloading and resource allocation based on DL-GA in mobile edge computing

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Turkish Journal of Electrical Engineering and Computer sciences 2023年第3期31卷 498-515页

作者： Gu, Hang Zhang, MinJuan Li, WenZao Pan, YuWen School of information and Communication Engineering North University of China Taiyuan China Educational Informationalization and Big Data Center Education Department of Sichuan Province Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

With the rapid development of 5G and the Internet of Things (IoT), the traditional cloud computing architecture struggle to support the booming computation-intensive and latency-sensitive applications. Mobile edge computing (MEC) has emerged as a solution which enables abundant IoT tasks to be offloaded to edge services. However, task offloading and resource allocation remain challenges in MEC framework. In this paper, we add the total number of offloaded tasks to the optimization objective and apply algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA) to maximize the value function, which is defined as a weighted sum of energy consumption, latency, and the number of offloaded tasks. First, we use GA to optimize the task offloading scheme and store the states and labels of scenario. Each state consists of five parameters: the IDs of all tasks generated in this scenario, the cost of each task, whether the task is offloaded, bandwidth occupied by offloaded task and remaining bandwidth of edge server. The labels are the tasks that are currently selected for offloading. Then, these states and labels will be used to train neural network. Finally, the trained neural network can quickly give optimization solutions. Simulation results show that DL-GA can execute 75 to 450 times faster than GA without losing much optimization power. At the same time, DL-GA has stronger optimization capability compared to Deep Q-Learning Network (DQN). © 2023 Turkiye Klinikleri. All rights reserved.

关键词： Genetic algorithms

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DecisionVis: A Method of Multi-attribute Correlation Visual Analysis Decision 22

DecisionVis: A Method of Multi-attribute Correlation Visual ...

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5th International Conference on big data Technologies, ICBDT 2022

作者： Chu, Qikai Wu, Yadong Zhang, Lingli He, Zhouqiao Qiu, Yu Sichuan University of Science and Engineering Sichuan Provincial Engineering Laboratory of Big Data Visual Analysis Technology China

ISBN: (纸本)9781450396875

In the process of fluorine material research and development, the traditional "manual"analysis method can no longer meet the needs of large and complex high-dimensional data analysis. This paper proposes DecisionVis, a multi-attribute correlation visual analysis decision method based on the data of multi-attribute influencing factors of fluorine chemical products, which supports the user to design and implement a multi-attribute correlation visual analysis decision system to explore the correlation between multi-attribute influencing factors of fluorine chemical products through the visual analysis of high-dimensional data and the visual analysis results of market price uncertainty using the 3D parallel coordinate technology with the premise of node connection. The system is designed and implemented to explore the correlation relation-ship between the factors influencing fluorine chemical products. Finally, case studies and interviews with experts from fluorochemical companies demonstrate the effectiveness and ease of use of this method for visual analysis of multi-attribute correlations of fluorochemical products. © 2022 ACM.

关键词： Uncertainty analysis

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Network Topology Inference with Sparsity and Laplacian Constraints 11

Network Topology Inference with Sparsity and Laplacian Const...

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2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Ying, Jiaxi Han, Xi Zhou, Rui Wang, Xiwen So, Hing Cheung Hong Kong University of Science and Technology Hong Kong North China University of Technology China Shenzhen Research Institute of Big Data China City University of HongKong Hong Kong

ISBN: (纸本)9798350314014

We tackle the network topology inference problem by utilizing Laplacian constrained Gaussian graphical models, which recast the task as estimating a precision matrix in the form of a graph Laplacian. Recent research [1] has uncovered the limitations of the widely used 1-norm in learning sparse graphs under this model: empirically, the number of nonzero entries in the solution grows with the regularization parameter of the 1-norm;theoretically, a large regularization parameter leads to a fully connected (densest) graph. To overcome these challenges, we propose a graph Laplacian estimation method incorporating the 0-norm constraint. An efficient gradient projection algorithm is developed to solve the resulting optimization problem, characterized by sparsity and Laplacian constraints. Through numerical experiments with synthetic and financial time-series datasets, we demonstrate the effectiveness of the proposed method in network topology inference. © 2023 IEEE.

关键词： Network topology

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An Efficient Blockchain-Based Certificateless Anonymous Authentication Scheme for VANETs

An Efficient Blockchain-Based Certificateless Anonymous Auth...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Bi, Changbing Tian, Youliang Li, Ta Wu, Shihong Zhou, Hua Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou University Guizhou Province Key Laboratory of Cryptography and Block Chain Technology Guiyang550025 China Institute of Cryptography & Data Security Guizhou University Guiyang550025 China College of Big Data and Information Engineering Guizhou University Guiyang550025 China

ISBN: (纸本)9798350327380

Vehicle ad-hoc networks (VANETs) play a critical role in providing security and privacy protection for intelligent transportation systems. However, the limited network bandwidth and computing capacity within VANETs have resulted in existing authentication schemes having excessive computational overhead, which cannot meet the low-latency communication requirements of VANETs. To tackle these problems, we propose an efficient blockchain-based certificateless anonymous authentication scheme for VANETs. Firstly, we propose a novel signature scheme without bilinear pairing to reduce the computational overhead in VANETs. Secondly, we utilize blockchain to facilitate trusted information sharing. Thirdly, we provide privacy-preserving through the use of anonymous keys, and enable tracing of vehicle identities in the event of malicious behavior. Finally, security analysis and simulation experiments demonstrate that our proposed scheme has lower communication and computational overhead, while satisfying various security requirements for VANETs, such as anonymity, unlinkability, and traceability. © 2023 IEEE.

关键词： Authentication

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Information Freshness in Coordinate Decision-Making Communication Systems

Information Freshness in Coordinate Decision-Making Communic...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhou, Siyuan Wang, Ziqing Dong, Yunquan Tang, Bin Tan, Guoping Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Hohai University College of Information Science and Engineering Nanjing China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing China

ISBN: (纸本)9798350351255

The promptness of decision-making is of paramount importance in coordinate decision-making communication systems. In this paper, we envision a system in which a sender transmits information to a receiver for decision-making. The decision-making process encompasses the extraction of decision-related information followed by subsequent computation. To assess the impact of communication delays, randomness of decision-making and computational time on the immediacy of decisions, we introduce a novel performance metric, namely the age of decision (AoD). Specifically, AoD is defined as the time elapsed since the generation of the update upon which the most recent decision is based. Considering the stochastic nature of decision-making, we analyze the average peak AoD in systems where the arrival and service processes follow general processes or Poisson processes. To facilitate our derivations, we introduce the concept of effective decisions to denote those decisions that can lead to the system's peak AoD. Simulation results confirm the validity of our derivations and illustrate that appropriately increasing the rate of decision-making can significantly reduce the system's average peak AoD. © 2024 IEEE.

关键词： Stochastic systems

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Densely stacked rebar counting based on image semantic segmentation in wide scene 3

Densely stacked rebar counting based on image semantic segme...

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3rd International conference on Computer, big data and Artificial Intelligence, ICCBDAI 2022

作者： Wang, Yi Chen, Duanbing Li, Wenrui Xue, Yu Deng, Xiang Zhou, Junlin Ren, Junsong Sichuan University of Science & Engineering Yibin644002 China Chengdu Union Big Data Tech. Inc. Chengdu610041 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China The First Construction Engineering Limited Company of China Construction Third Engineering Bureau Wuhan430074 China

To accurately count densely stacked rebars belongs to semantic segmentation in image. Various methods have been proposed to segment the objects in single image. However, widely and densely stacking scene such as warehouse or vehicle stacking of rebars is always faced. For these scenes, clear image of end faces of rebars cannot be obtained through a single camera. Generally, multiple cameras are often used to take clear images to count rebars. One strategy is to stitch multiple images into a single image at pixel level first, and then count rebars in this stitched image. Various methods have been proposed to stitch images, but it is extremely difficult to stitch two images at pixel level accurately for parallax distortion or partially hidden. A topological stitching strategy is proposed in this paper to count the rebars in whole scene. In the method, rebars in each image are identified and counted first, and then the identification and counting results in the adjacent images are stitched so as to esitmate the total number of rebars. The perfomance of the method is evaluated on real datasets, and the counting accuracy exceeds 99%. Experiments show that the proposed method is effective on widely and densely stacked rebars identification. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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A Symbolic Rule Integration Framework with Logic Transformer for Inductive Relation Prediction 24

A Symbolic Rule Integration Framework with Logic Transformer...

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33rd ACM Web Conference, WWW 2024

作者： Pan, Yudai Liu, Jun Zhao, Tianzhe Zhang, Lingling Lin, Yun Dong, Jin Song School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'an China Shanghai Jiao Tong University Shanghai China National University of Singapore Singapore Singapore

ISBN: (纸本)9798400701719

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm by KG embeddings has a limitation to predict the relation between unseen entities. This situation is called an inductive setting, which is more common in the real-world scenario. To handle this issue, implicit symbolic rules have shown great potential in capturing the inductive capability. However, it is still challenging to obtain precise representations of logic rules from KGs. The argument variability and predicate non-commutativity in symbolic rule integration make the modeling of component symbols difficult. To this end, we propose a novel inductive relation prediction model named SymRITa with a logic transformer integrating rules. SymRITa firstly extracts the subgraph, whose embeddings are captured by a graph network. Meanwhile, symbolic rule graphs in the subgraph can be generated. Then, the symbolic rules are modeled by a proposed logic transformer. Specifically, the input format based on the subgraph-based embeddings is to focus on the argument variability in symbolic rules. In addition, a conjunction attention mechanism in the logic transformer can resolve predicate non-commutativity in the symbolic rule integration process. Finally, the subgraph-based and symbol-based embeddings obtained from the previous steps are combined for the training regime, and prediction results as well as rules explaining the reasoning process are explicitly output. Extensive experiments on twelve inductive datasets show that SymRITa achieves outstanding effectiveness compared to state-of-the-art inductive baselines. Moreover, the logic rules with corresponding confidences provide an interpretable paradigm. © 2024 ACM.

关键词： Forecasting

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