Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016511
Li Ke, Liao Jie, Wan Wangjun
Non-autoregressive translation (NAT) has become a hot direction for its acceleration on decoding. Conditional masked language model (CMLM) performs excellently in NAT models. We review and extend the CMLM in some strategy: (1) N-gram mask strategy, which can help model to learn coarse sematic information of target language; (2) top-k decoding strategy, our model generates the top-k probability words in each step so that it can generate the final sentence in constant number steps. Extensive experiments demonstrate that our method is progressive compared with CMLM and some other NAT models. Specially, on the dataset WMT 14 EN-DE, our approach can achieve 27.24 BLEU score with only 0.1 BLEU sacrifice compared with the autoregressive counterpart base Transformer while speeding up 3 times on decoding.
{"title":"Non-Autoregressive Machine Translation with a Novel Masked Language Model","authors":"Li Ke, Liao Jie, Wan Wangjun","doi":"10.1109/ICCWAMTIP56608.2022.10016511","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016511","url":null,"abstract":"Non-autoregressive translation (NAT) has become a hot direction for its acceleration on decoding. Conditional masked language model (CMLM) performs excellently in NAT models. We review and extend the CMLM in some strategy: (1) N-gram mask strategy, which can help model to learn coarse sematic information of target language; (2) top-k decoding strategy, our model generates the top-k probability words in each step so that it can generate the final sentence in constant number steps. Extensive experiments demonstrate that our method is progressive compared with CMLM and some other NAT models. Specially, on the dataset WMT 14 EN-DE, our approach can achieve 27.24 BLEU score with only 0.1 BLEU sacrifice compared with the autoregressive counterpart base Transformer while speeding up 3 times on decoding.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122910998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016489
Guo Tiantian, E. Lim, M. López-Benítez, Ma Fei, Yu Limin
Active pulse design, target detection and classification play an essential role in underwater acoustic sensing. This paper addresses the system design with three kinds of pulse signals, including continuous wave (CW), linear frequency modulation (LFM) signal and rational orthogonal wavelet (ROW) signal. The detector design has an architecture of feature extraction and convolutional neural network (CNN) based classification. A geometric underwater channel model is adopted to facilitate the generation of training datasets with designated geometric underwater environment parameters. The simulated received pulse signals are converted into feature maps as the input of the classifier. This paper applies the acoustic features, Short Time Fourier Transform (STFT), Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) to construct different feature maps. A lightweight CNN model is used as the classifier. Experiments demonstrate the superiority of the ROW wavelet pulse signals and the proposed algorithm in target localization and underwater signal classification.
{"title":"Underwater Acoustic Sensing with Rational Orthogonal Wavelet Pulse and Auditory Frequency Cepstral Coefficient-Based Feature Extraction","authors":"Guo Tiantian, E. Lim, M. López-Benítez, Ma Fei, Yu Limin","doi":"10.1109/ICCWAMTIP56608.2022.10016489","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016489","url":null,"abstract":"Active pulse design, target detection and classification play an essential role in underwater acoustic sensing. This paper addresses the system design with three kinds of pulse signals, including continuous wave (CW), linear frequency modulation (LFM) signal and rational orthogonal wavelet (ROW) signal. The detector design has an architecture of feature extraction and convolutional neural network (CNN) based classification. A geometric underwater channel model is adopted to facilitate the generation of training datasets with designated geometric underwater environment parameters. The simulated received pulse signals are converted into feature maps as the input of the classifier. This paper applies the acoustic features, Short Time Fourier Transform (STFT), Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) to construct different feature maps. A lightweight CNN model is used as the classifier. Experiments demonstrate the superiority of the ROW wavelet pulse signals and the proposed algorithm in target localization and underwater signal classification.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127678300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016480
Su Jin, Lyu Shubin, Lu Xin, Wan You, Liao Fusheng
Job shop scheduling problem (JSP) is a very general and complex scheduling problem in the manufacturing industry. The traditional priority dispatching rule (PDR) can get the approximate solution for some specific problems. Nevertheless, for the complex and changing realistic factory floor, the quality of the existing solution fluctuates significantly. To solve the problem, this paper fuse multiple dispatching rules and the Insertion Greedy Algorithm (IGA) to deep reinforcement learning (DRL), namely RIDRL, to solve the job shop scheduling problem. In this method, we manually choose five generalizable state features as the states of the workshop environment. Employing 18 scheduling rules as the action space in the agent while designing a quick converge reward function. Additionally, we use a Proximal Policy Optimization Algorithm (PPO) to train the DRL agent with minimizing makespan as the optimization objective. Several simulation experiments on many standard instances indicate that the proposed method obtains competitive solutions for problems of different sizes.
{"title":"RIDRL: A Deep Reinforcement Learning Based on Multiple Dispatching Rules and IGA Algorithm for JSP","authors":"Su Jin, Lyu Shubin, Lu Xin, Wan You, Liao Fusheng","doi":"10.1109/ICCWAMTIP56608.2022.10016480","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016480","url":null,"abstract":"Job shop scheduling problem (JSP) is a very general and complex scheduling problem in the manufacturing industry. The traditional priority dispatching rule (PDR) can get the approximate solution for some specific problems. Nevertheless, for the complex and changing realistic factory floor, the quality of the existing solution fluctuates significantly. To solve the problem, this paper fuse multiple dispatching rules and the Insertion Greedy Algorithm (IGA) to deep reinforcement learning (DRL), namely RIDRL, to solve the job shop scheduling problem. In this method, we manually choose five generalizable state features as the states of the workshop environment. Employing 18 scheduling rules as the action space in the agent while designing a quick converge reward function. Additionally, we use a Proximal Policy Optimization Algorithm (PPO) to train the DRL agent with minimizing makespan as the optimization objective. Several simulation experiments on many standard instances indicate that the proposed method obtains competitive solutions for problems of different sizes.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116974848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016575
Li Hengbai
Due to the unique challenges of high intra-class variation and low inter-class variation, Fine-Grained Visual Classification (FGVC) tasks extremely need to extract multilevel semantic features of fine-grained images for classification. Moreover, labor-intensive annotations and the existence of long-tailed distribution of fine-grained images in real life make fine-grained few-shot learning an urgent problem to be solved. In this paper, we propose an Adaptive Weighting Pyramidal Convolutional Neural Network (AW-PCNN) for fine-grained few-shot learning. Our AW-PCNN consists of a PCNN module and a AW module, which are improved in two aspects. First, our PCNN module extracts the features of each layer in CNN to obtain both high-level global and low-level local subtle features of images to overcome the challenge of FGVC tasks. Second, We employ the metric learning approach for few-shot learning, and our AW module improves it by selecting decisive pairs and adaptively weighting the pairs based on their similarity scores to mitigate the challenge of FGVC tasks and learn a better embedding space. Our AW-PCNN achieves state-of-the-art performance on three benchmark fine-grained datasets, which proves the effectiveness and superiority of our model.
{"title":"AW-PCNN: Adaptive Weighting Pyramidal Convolutional Neural Network for Fine-Grained Few-Shot Learning","authors":"Li Hengbai","doi":"10.1109/ICCWAMTIP56608.2022.10016575","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016575","url":null,"abstract":"Due to the unique challenges of high intra-class variation and low inter-class variation, Fine-Grained Visual Classification (FGVC) tasks extremely need to extract multilevel semantic features of fine-grained images for classification. Moreover, labor-intensive annotations and the existence of long-tailed distribution of fine-grained images in real life make fine-grained few-shot learning an urgent problem to be solved. In this paper, we propose an Adaptive Weighting Pyramidal Convolutional Neural Network (AW-PCNN) for fine-grained few-shot learning. Our AW-PCNN consists of a PCNN module and a AW module, which are improved in two aspects. First, our PCNN module extracts the features of each layer in CNN to obtain both high-level global and low-level local subtle features of images to overcome the challenge of FGVC tasks. Second, We employ the metric learning approach for few-shot learning, and our AW module improves it by selecting decisive pairs and adaptively weighting the pairs based on their similarity scores to mitigate the challenge of FGVC tasks and learn a better embedding space. Our AW-PCNN achieves state-of-the-art performance on three benchmark fine-grained datasets, which proves the effectiveness and superiority of our model.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115337472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016507
Mohamed Ali Setitra, Ilyas Benkhaddra, Zine El Abidine Bensalem, Mingyu Fan
Distributed Denial of Service (DDoS) attacks are one of the most significant challenges in network security, especially in the Software-Defined Network (SDN) environment, due to the centralized network management provided by the Control Plane. Considering the insufficiency of traditional detection approaches because of the growth and sophistication of DDoS attacks, exploiting Machine Learning (ML) techniques is in high demand. For this, feature modeling is essential to obtain an effective ML-based DDoS detection system, especially in the pre-processing phase. In this paper, we proposed and implemented a pre-processing model based on deep studying the dataset, going so far as to increase the features number for a better representation and, if necessary, minimize the data dimension by exploring some dimensionality reduction techniques such as Principal Component Analysis (PCA) or t-distributed Stochastic Neighbor Embedding (t-SNE). Moreover, to invest even more in our conceptual aspect relating to SDN environments, as specified in the above-cited challenge, we have chosen to implement our proposed model using an open-source SDN dataset created specially in an SDN environment. Then, the statistical characteristics of these correlations are analyzed. In addition, eight ML techniques between supervised and unsupervised models were used in our work to detect DDoS attacks. Finally, we compared our proposed model with other existing approaches. The outcome showed that the detecting reliability is improved, and the method has a good effect on detecting DDoS attacks compared with other methods.
{"title":"Feature Modeling and Dimensionality Reduction to Improve ML-Based DDOS Detection Systems in SDN Environment","authors":"Mohamed Ali Setitra, Ilyas Benkhaddra, Zine El Abidine Bensalem, Mingyu Fan","doi":"10.1109/ICCWAMTIP56608.2022.10016507","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016507","url":null,"abstract":"Distributed Denial of Service (DDoS) attacks are one of the most significant challenges in network security, especially in the Software-Defined Network (SDN) environment, due to the centralized network management provided by the Control Plane. Considering the insufficiency of traditional detection approaches because of the growth and sophistication of DDoS attacks, exploiting Machine Learning (ML) techniques is in high demand. For this, feature modeling is essential to obtain an effective ML-based DDoS detection system, especially in the pre-processing phase. In this paper, we proposed and implemented a pre-processing model based on deep studying the dataset, going so far as to increase the features number for a better representation and, if necessary, minimize the data dimension by exploring some dimensionality reduction techniques such as Principal Component Analysis (PCA) or t-distributed Stochastic Neighbor Embedding (t-SNE). Moreover, to invest even more in our conceptual aspect relating to SDN environments, as specified in the above-cited challenge, we have chosen to implement our proposed model using an open-source SDN dataset created specially in an SDN environment. Then, the statistical characteristics of these correlations are analyzed. In addition, eight ML techniques between supervised and unsupervised models were used in our work to detect DDoS attacks. Finally, we compared our proposed model with other existing approaches. The outcome showed that the detecting reliability is improved, and the method has a good effect on detecting DDoS attacks compared with other methods.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114787233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016540
Teng Shuhua, Zheng Lidong, Cheng Zhengting, Yuan Zhian, Ma Yanxin
Panoramic segmentation is an important research direction in computer vision. Considering that different applications have different requirements for semantic segmentation accuracy, a semantic enhancement loss function based on attention mechanism is proposed. By adding attention mechanism, it can enhance the sensitivity to the semantic information of task attention and improve the classification accuracy of specific objects and backgrounds. The experimental results show that the semantic enhancement loss function can effectively improve the classification accuracy of semantic categories required by tasks.
{"title":"Semantic Enhancement Loss Function Based on Attention Mechanism","authors":"Teng Shuhua, Zheng Lidong, Cheng Zhengting, Yuan Zhian, Ma Yanxin","doi":"10.1109/ICCWAMTIP56608.2022.10016540","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016540","url":null,"abstract":"Panoramic segmentation is an important research direction in computer vision. Considering that different applications have different requirements for semantic segmentation accuracy, a semantic enhancement loss function based on attention mechanism is proposed. By adding attention mechanism, it can enhance the sensitivity to the semantic information of task attention and improve the classification accuracy of specific objects and backgrounds. The experimental results show that the semantic enhancement loss function can effectively improve the classification accuracy of semantic categories required by tasks.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127009978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016544
Cao Chenchen, X. Chunxiang, Jiang Changsong, Han Yunxia
Smart grid utilizes intelligent metering devices that transmit the energy usage data of an end user to a service provider. During the transmission’ an adversary could eavesdrop’ intercept’ modify and forge the data. Therefore’ it is critically important to make sure that such data is confidential and unforgeable. Signcryption is an effective technique to satisfy the requirements. However’ existing signcryption schemes are generally based on conventional hardness assumptions’ which are vulnerable to adversaries equipped with quantum computers in the near future. Additionally, they fail to consider identity leakage that allows an adversary to target a special end-user group of smart grid for attacks. To solve the problems’ an anonymous signcryption scheme for smart grid, dubbed AS4Smd, is proposed. AS4Smd is based on lattice cryptography’ which is post-quantum secure. In AS4Smd’ an end-user can sign and encrypt data in a logically single step to produce a signcryptext. Additionally’ the end-user’s identity is embedded in the signcryptext to accomplish end-user anonymity. The performance evaluation on AS4Smd shows that it is efficient in terms of computation and communication efficiency.
{"title":"Lattice-Based Anonymous Signcryption For Smart Grid","authors":"Cao Chenchen, X. Chunxiang, Jiang Changsong, Han Yunxia","doi":"10.1109/ICCWAMTIP56608.2022.10016544","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016544","url":null,"abstract":"Smart grid utilizes intelligent metering devices that transmit the energy usage data of an end user to a service provider. During the transmission’ an adversary could eavesdrop’ intercept’ modify and forge the data. Therefore’ it is critically important to make sure that such data is confidential and unforgeable. Signcryption is an effective technique to satisfy the requirements. However’ existing signcryption schemes are generally based on conventional hardness assumptions’ which are vulnerable to adversaries equipped with quantum computers in the near future. Additionally, they fail to consider identity leakage that allows an adversary to target a special end-user group of smart grid for attacks. To solve the problems’ an anonymous signcryption scheme for smart grid, dubbed AS4Smd, is proposed. AS4Smd is based on lattice cryptography’ which is post-quantum secure. In AS4Smd’ an end-user can sign and encrypt data in a logically single step to produce a signcryptext. Additionally’ the end-user’s identity is embedded in the signcryptext to accomplish end-user anonymity. The performance evaluation on AS4Smd shows that it is efficient in terms of computation and communication efficiency.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123077914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016495
Yiheng Huang, Lei Huang, Hongguang Fu
Deep generative models used to generate molecules have demonstrated their superior performance in the creation of novel structures. However, mode collapse is a severe and frequent issue in the GAN-based molecule generation models, in which generator learns a few modes of the data distribution while ignoring others. In this work, we introduce Mol Manifold Guidance Generative Adversarial Network (Mol-MGGAN) to solve this problem. Mol-MGGAN extends generative adversarial networks by introducing a manifold guidance network, which contains a graph encoder that maps molecules into a latent manifold space that covers overall modes of the data distribution, and a discriminator that distinguishes molecules in the manifold space. The guidance network can explicitly prevent the generator from mode collapse through forcing the generator to learn the overall modes of the data. We use the genetic algorithm to further enhance the generator’s ability to produce novel and unique molecules. In the experiments on the QM9 chemical database, we demonstrate that Mol-MGGAN generates nearly 100% valid molecules. Most importantly, we generate more unique and novel molecules compared to the previous GAN-based molecule generation model. The result of the experiments shows that Mol-MGGAN reduces mode collapse during the molecular generation.
{"title":"Automatic Molecule Generation Using Manifold Guidance GAN and Genetic Algorithm","authors":"Yiheng Huang, Lei Huang, Hongguang Fu","doi":"10.1109/ICCWAMTIP56608.2022.10016495","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016495","url":null,"abstract":"Deep generative models used to generate molecules have demonstrated their superior performance in the creation of novel structures. However, mode collapse is a severe and frequent issue in the GAN-based molecule generation models, in which generator learns a few modes of the data distribution while ignoring others. In this work, we introduce Mol Manifold Guidance Generative Adversarial Network (Mol-MGGAN) to solve this problem. Mol-MGGAN extends generative adversarial networks by introducing a manifold guidance network, which contains a graph encoder that maps molecules into a latent manifold space that covers overall modes of the data distribution, and a discriminator that distinguishes molecules in the manifold space. The guidance network can explicitly prevent the generator from mode collapse through forcing the generator to learn the overall modes of the data. We use the genetic algorithm to further enhance the generator’s ability to produce novel and unique molecules. In the experiments on the QM9 chemical database, we demonstrate that Mol-MGGAN generates nearly 100% valid molecules. Most importantly, we generate more unique and novel molecules compared to the previous GAN-based molecule generation model. The result of the experiments shows that Mol-MGGAN reduces mode collapse during the molecular generation.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123261367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/iccwamtip56608.2022.10016584
{"title":"ICCWAMTIP 2022 Cover Page","authors":"","doi":"10.1109/iccwamtip56608.2022.10016584","DOIUrl":"https://doi.org/10.1109/iccwamtip56608.2022.10016584","url":null,"abstract":"","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121145828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016514
Collins Sey, Hang Lei, Xiaoyu Li, Weizhong Qian, Obed Barnes, Linda Delali Fiasam, Cong Zhang, Seth Larweh Kodjiku, Isaac Osei Agyemang, Isaac Adjei-Mensah, Xiaolei Shang
The Internet of Things (IoT) has become increasingly popular due to the enormous growth in the number of smart devices and the massive amount of data these devices generate. Data access and sharing has been one of the most valuable services of the IoT network. For this reason, the security and privacy of the data are of great essence to harnessing the full potential of the IoT network. Existing security measures for data access and sharing have proven to be insufficient. They usually need more credibility due to centralization and single-point-of-failure problems. In this paper, we propose WB-Proxshare, a data access and sharing model based on warrant and proxy re-encryption (PRE) with blockchain. We present a mechanism that further enhances collusion-resistance in data storage, access and sharing. Our model ensures tamper-proof, data privacy, provenance and auditing. We set a proxy server that issues warrant for data storage and sharing and re-encrypts data owners’ encrypted data to grant access to legitimate data users. Our security analysis and evaluation show that the proposed model has high efficiency and ensures strong data security, integrity, and confidentiality guarantee for data access and sharing in the IoT environment and is practicable.
{"title":"Wb-Proxshare: A Warrant-Based Proxy Re-Encryption Model for Secure Data Sharing in Iot Networks Via Blockchain","authors":"Collins Sey, Hang Lei, Xiaoyu Li, Weizhong Qian, Obed Barnes, Linda Delali Fiasam, Cong Zhang, Seth Larweh Kodjiku, Isaac Osei Agyemang, Isaac Adjei-Mensah, Xiaolei Shang","doi":"10.1109/ICCWAMTIP56608.2022.10016514","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016514","url":null,"abstract":"The Internet of Things (IoT) has become increasingly popular due to the enormous growth in the number of smart devices and the massive amount of data these devices generate. Data access and sharing has been one of the most valuable services of the IoT network. For this reason, the security and privacy of the data are of great essence to harnessing the full potential of the IoT network. Existing security measures for data access and sharing have proven to be insufficient. They usually need more credibility due to centralization and single-point-of-failure problems. In this paper, we propose WB-Proxshare, a data access and sharing model based on warrant and proxy re-encryption (PRE) with blockchain. We present a mechanism that further enhances collusion-resistance in data storage, access and sharing. Our model ensures tamper-proof, data privacy, provenance and auditing. We set a proxy server that issues warrant for data storage and sharing and re-encrypts data owners’ encrypted data to grant access to legitimate data users. Our security analysis and evaluation show that the proposed model has high efficiency and ensures strong data security, integrity, and confidentiality guarantee for data access and sharing in the IoT environment and is practicable.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133522271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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