Journal of Networking and Network Applications最新文献_第2页

PreBiGE: Course Recommendation Using Course Prerequisite Relation Embedding and Bipartite Graph Embedding PreBiGE:使用课程前提关系嵌入和二部图嵌入进行课程推荐

Journal of Networking and Network Applications Pub Date : 1900-01-01 DOI: 10.33969/j-nana.2022.020404

Hafsa Kabir Ahmad, Bo Liu, Bello Ahmad Muhammad, M. Umar

{"title":"PreBiGE: Course Recommendation Using Course Prerequisite Relation Embedding and Bipartite Graph Embedding","authors":"Hafsa Kabir Ahmad, Bo Liu, Bello Ahmad Muhammad, M. Umar","doi":"10.33969/j-nana.2022.020404","DOIUrl":"https://doi.org/10.33969/j-nana.2022.020404","url":null,"abstract":"A growing number of students enrol in online education to improve their skills. However, students are faced with the challenge of finding courses that meet their individual needs. Recommender systems were introduced to help students choose the courses that best meet their needs. To learn better representations of students and courses for improved recommendation results, existing graph-based recommender systems utilize the high-order collaborative signals between set of students or set of courses from a bipartite graph. However, courses also have prerequisite dependency between them, which when utilized together with collaborative relations can improve recommendation results. On this basis, we propose a model that utilizes the high-order relation between set of courses, the prerequisite dependency between courses, as well as the direct relation between students and courses. Using meta-paths generated from the knowledge graph, our model extracts the prerequisite dependency between courses, which is then used to generate a course prerequisite graph. The course prerequisite graph and the student-course bipartite graph are used to learn the representation of the students and courses, jointly capturing the prerequisite dependency, high-order collaborative relations as well as direct relations. The learned representations are used for recommendation. The experiments on real-world dataset show the superiority of our proposed method, achieving 3.61% on F1@10 and 1.38% on Mrr@10.","PeriodicalId":384373,"journal":{"name":"Journal of Networking and Network Applications","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132010914","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}

引用次数: 0

GFS-CNN: A GPU-friendly Secure Computation Platform for Convolutional Neural Networks GFS-CNN:一个gpu友好的卷积神经网络安全计算平台

Journal of Networking and Network Applications Pub Date : 1900-01-01 DOI: 10.33969/j-nana.2023.030202

Chao Guo, Ke Cheng, Jiaxuan Fu, Ruolu Fan, Zhao Chang, Zhiwei Zhang, Anxiao Song

{"title":"GFS-CNN: A GPU-friendly Secure Computation Platform for Convolutional Neural Networks","authors":"Chao Guo, Ke Cheng, Jiaxuan Fu, Ruolu Fan, Zhao Chang, Zhiwei Zhang, Anxiao Song","doi":"10.33969/j-nana.2023.030202","DOIUrl":"https://doi.org/10.33969/j-nana.2023.030202","url":null,"abstract":"Outsourcing convolutional neural network (CNN) inference services to the cloud is extremely beneficial, yet raises critical privacy concerns on the proprietary model parameters of the model provider and the private input data of the user. Previous studies have indicated that some cryptographic tools such as secure multi-party computation (MPC) can be used to achieve secure outsourced inferences. However, MPC-based approaches often require a large number of communication rounds across two or more non-colluding servers, which make them hard to exploit GPU acceleration. In this paper, we propose GFS-CNN, a GPU-friendly secure computation platform for convolutional neural networks. The following two specific efforts of GFS-CNN have been made by combining machine learning and cryptography techniques. Firstly, We use quadratic activation functions to replace most of the ReLU functions without losing much accuracy, so as to create a mixed linear layer for better efficiency by integrating convolution, batch normalization, and quadratic activation. Secondly, for the rest ReLU functions, we implement the secure ReLU protocol using function secret sharing, enabling GFS-CNN to evaluate the secure comparison function via a single interaction during the online phase. Extensive experiments demonstrate that GFS-CNN is accuracy-preserving and reduces online inference time by 16.4% on VGG-16 models compared to Delphi (USENIX Security’20).","PeriodicalId":384373,"journal":{"name":"Journal of Networking and Network Applications","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131634477","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}

引用次数: 0

Privacy-Utility Equilibrium Protocol for Federated Aggregating Multiparty Genome Data 联合聚合多方基因组数据的隐私效用平衡协议

Journal of Networking and Network Applications Pub Date : 1900-01-01 DOI: 10.33969/j-nana.2021.010303

Hai Liu, Changgen Peng, Youliang Tian, Feng Tian, Zhenqiang Wu

{"title":"Privacy-Utility Equilibrium Protocol for Federated Aggregating Multiparty Genome Data","authors":"Hai Liu, Changgen Peng, Youliang Tian, Feng Tian, Zhenqiang Wu","doi":"10.33969/j-nana.2021.010303","DOIUrl":"https://doi.org/10.33969/j-nana.2021.010303","url":null,"abstract":"Cloud server aggregates a large amount of genome data from multi genome donors to facilitate scientific research. However, the untrusted cloud server is prone to violate privacy of aggregating genome data. Thus, each genome donor can randomly perturb her genome data using differential privacy mechanism before aggregating. But this is easy to lead to utility disaster of aggregating genome data due to the different privacy preferences of each genome donor, and privacy leakage of aggregating genome data because of the kinship between genome donors. The key challenge here is to achieve an equilibrium between privacy preserving and data utility of aggregating multiparty genome data. To this end, we proposed federated aggregation protocol of multiparty genome data (MGD-FAP) with privacy-utility equilibrium for guaranteeing desired privacy protection and desired data utility. First, we regarded the privacy budget and the accuracy as the desired privacy-utility metrics of genome data respectively. Second, we constructed the federated aggregation model of multiparty genome data by combining random perturbation method of genome data guaranteeing desired data utility with federated comparing update method of local privacy budget achieving desired privacy preserving. Third, we presented the MGD-FAP maintaining privacy-utility equilibrium under the federated aggregation model of multiparty genome data. Finally, our theoretical and experimental analysis showed that MGD-FAP can maintain privacy-utility equilibrium. The MGD-FAP is practical and feasible to ensure the privacy-utility equilibrium of cloud server aggregating multiparty genome data.","PeriodicalId":384373,"journal":{"name":"Journal of Networking and Network Applications","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132686060","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}

引用次数: 0

A Range Query Method for Data Access Pattern Protection Based on Uniform Access Frequency Distribution 基于均匀访问频率分布的数据访问模式保护范围查询方法

Journal of Networking and Network Applications Pub Date : 1900-01-01 DOI: 10.33969/j-nana.2023.030102

Jing Yan, Zhao Chang, Ke Cheng, Shuguang Wang

{"title":"A Range Query Method for Data Access Pattern Protection Based on Uniform Access Frequency Distribution","authors":"Jing Yan, Zhao Chang, Ke Cheng, Shuguang Wang","doi":"10.33969/j-nana.2023.030102","DOIUrl":"https://doi.org/10.33969/j-nana.2023.030102","url":null,"abstract":"Data encryption is necessary to keep user information secure and private on the cloud. However, adversaries can still learn valuable information about the encrypted data by observing data access patterns. To solve this issue, Oblivious RAMs (ORAMs) are proposed to hide access patterns. However, ORAMs are expensive and not suitable for deployment in a large database. In this work, we propose a range query algorithm while providing data access pattern protection based on uniform access frequency. In the preprocessing, multiple key-value pairs in the database are grouped and stored in each storage module, and we make copies for frequently accessed key-value pairs and also add some dummy key-value pairs on each storage module. In the online query processing, according to the range query length of the received query access request, we visit the specific storage module for the query and obtain the query result. Based on the techniques above, our method makes the uniform distribution of access frequency of data blocks in the database and achieves a security guarantee as strong as the state-of-the-art method. Compared with data queries that do not provide data access pattern protection, the ratio of network communication overhead is constant rather than logarithmic in ORAMs.","PeriodicalId":384373,"journal":{"name":"Journal of Networking and Network Applications","volume":"38 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120823302","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}

引用次数: 0

Dynamic SIoT Network Status Prediction 动态SIoT网络状态预测

Journal of Networking and Network Applications Pub Date : 1900-01-01 DOI: 10.33969/j-nana.2022.020203

Dong Hu, Shuai Lyu, Shih Yu Chang, Limei Peng, P. Ho

{"title":"Dynamic SIoT Network Status Prediction","authors":"Dong Hu, Shuai Lyu, Shih Yu Chang, Limei Peng, P. Ho","doi":"10.33969/j-nana.2022.020203","DOIUrl":"https://doi.org/10.33969/j-nana.2022.020203","url":null,"abstract":"Prediction of social IoT (SIoT) data traffic is helpful in characterizing the complicated relationships for such as device-to-device, user-to-user, and user-to-device. One of the most popular traffic prediction methods in noisy environments is the Kalman filter (KF), which is extremely simple and general. Nevertheless, KF requires a dynamic traffic and measurement model as a priori, which introduces extra overhead and is often difficult to obtain in reality. In comparison, deep learning models with a Recurrent Neural Network (RNN) structure have been used extensively in modeling dynamic models evolving over time. On the other hand, the Content Adaptive Recurrent Unit (CARU) is an improvement of RNN that uses fewer parameters than the LSTM and GRU and thus is more promising in predicting the SIoT data traffic. This paper proposes the CARU-based extended Kalman filter (CARU-EKF) model, which is a new deep learning cell that utilizes CARU to predict extended Kalman filter (EKF) system parameters. Note that EKF is proper to predict nonlinear SIoT traffic in noisy environments. The proposed CARU-EKF can improve the performance of time-series data forecasting for nonlinear SIoT data traffic. Numerical experiments are conducted to evaluate the SIoT traffic prediction performance of the proposed CARU-EKF approach over two real datasets, i.e., IoT device traffic and wikipedia webpage visiting traffic. The proposed method shows better performance than existing prediction methods in terms of metrics of Mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and determination coefficient (R2).","PeriodicalId":384373,"journal":{"name":"Journal of Networking and Network Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127000423","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}

引用次数: 0

GenSelfHolding: Fusing Selfish Mining and Block Withholding Attacks on Bitcoin Revisited GenSelfHolding:重新审视比特币的自私挖矿和区块扣留攻击

Journal of Networking and Network Applications Pub Date : 1900-01-01 DOI: 10.33969/j-nana.2022.020102

Xuewen Dong, Sheng Gao

{"title":"GenSelfHolding: Fusing Selfish Mining and Block Withholding Attacks on Bitcoin Revisited","authors":"Xuewen Dong, Sheng Gao","doi":"10.33969/j-nana.2022.020102","DOIUrl":"https://doi.org/10.33969/j-nana.2022.020102","url":null,"abstract":"Due to the monetary value of Bitcoin, the most influential digital cryptocurrency in the world, Bitcoin has naturally become a valuable target of attacks, resulting in the emergence of many attack strategies on it. Among those attack strategies, selfish mining and block withholding attacks are two typical ones and attackers can obtain higher revenues under certain conditions than with an honest mining strategy. However, the combination of them will be a new type and more serious attack, which has not been analyzed in depth. In this paper, we propose GenSelfHolding, a general combined attack model with one selfish mining pool and random multiple honest pools on Bitcoin. Based on Markov chain, a general state transition graph and a general state distribution probability are presented to describe the internal features of our model. A general principle is then provided to calculate the attacker’s revenue. In addition, we give a detailed proof of the unique stable distribution of state transition probabilities. Such proof is an essential prerequisite for us to further present stable attacker revenue expressions under two specific scenarios, the GenSelfHolding model with two/three honest mining pools. Simulation results validate that the revenues of the attacker in these two specific models can reach up to 40% higher than those of classic selfish attackers in some cases.","PeriodicalId":384373,"journal":{"name":"Journal of Networking and Network Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130521843","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}

引用次数: 0

Machine Learning with Distributed Processing using Secure Divided Data: Towards Privacy-Preserving Advanced AI Processing in a Super-Smart Society 使用安全分割数据进行分布式处理的机器学习:在超级智能社会中实现保护隐私的高级人工智能处理

Journal of Networking and Network Applications Pub Date : 1900-01-01 DOI: 10.33969/j-nana.2022.020105

H. Miyajima, Noritaka Shigei, H. Miyajima, N. Shiratori

{"title":"Machine Learning with Distributed Processing using Secure Divided Data: Towards Privacy-Preserving Advanced AI Processing in a Super-Smart Society","authors":"H. Miyajima, Noritaka Shigei, H. Miyajima, N. Shiratori","doi":"10.33969/j-nana.2022.020105","DOIUrl":"https://doi.org/10.33969/j-nana.2022.020105","url":null,"abstract":"Towards the realization of a super-smart society, AI analysis methods that preserve the privacy of big data in cyberspace are being developed. From the viewpoint of developing machine learning as a secure and safe AI analysis method for users, many studies have been conducted in this field on 1) secure multiparty computation (SMC), 2) quasi-homomorphic encryption, and 3) federated learning, among other techniques. Previous studies have shown that both security and utility are essential for machine learning using confidential data. However, there is a trade-off between these two properties, and there are no known methods that satisfy both simultaneously at a high level. In this paper, as a superior method in both privacy-preserving of data and utility, we propose a learning method based on distributed processing using simple, secure, divided data and parameters. In this method, individual data and parameters are divided into multiple pieces using random numbers in advance, and each piece is stored in each server. The learning of the proposed method is achieved by using these data and parameters as they are divided and by repeating partial computations on each server and integrated computations at the central server. The advantages of the proposed method are the preservation of data privacy by not restoring the data and parameters during learning; the improvement of usability by realizing a machine learning method based on distributed processing, as federated learning does; and almost no degradation in accuracy compared to conventional methods. Based on the proposed method, we propose backpropagation and neural gas (NG) algorithms as examples of supervised and unsupervised machine learning applications. Our numerical simulation shows that these algorithms can achieve accuracy comparable to conventional models.","PeriodicalId":384373,"journal":{"name":"Journal of Networking and Network Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133576250","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}

引用次数: 4

High-altitude Multi-object Detection and Tracking based on Drone Videos 基于无人机视频的高空多目标检测与跟踪

Journal of Networking and Network Applications Pub Date : 1900-01-01 DOI: 10.33969/j-nana.2022.020103

Qiang Zhao, Limei Peng

{"title":"High-altitude Multi-object Detection and Tracking based on Drone Videos","authors":"Qiang Zhao, Limei Peng","doi":"10.33969/j-nana.2022.020103","DOIUrl":"https://doi.org/10.33969/j-nana.2022.020103","url":null,"abstract":"Drone videos have more extensive shooting ranges, more angles, and no geographical limitations. Thus the object detection algorithm based on drone videos is increasingly playing a role in various fields, such as military surveillance, space remote sensing, smart city, disaster monitoring scenes, etc. Compared to low-altitude object detection and tracking (LA-ODT), high-altitude object detection and tracking (HA-ODT) are receiving increasing attention, especially in modern cities with massive high buildings, because of their higher flying h eight, w ider v iewing a ngle, a nd t he a bility t o t rack multiple f ast-moving o bjects s imultaneously. However, high-altitude aerial videos (HA-AVs) are constrained by small objects that can be measured, fewer feature points, occlusions, and light changes. Therefore, HA-AVs suffer from blurry images with fewer feature points of objects and missed detection due to occlusion, degrading the ODT accuracy. Since the accessible HA datasets are very limited, not to mention featured datasets considering angles, weather, etc., this paper directly uses drones to collect HA pictures and videos of different angles, different illuminations, and different heights for self-labeling training. Regarding this, we adopt super-resolution reconstruction to increase the data diversity and add artificial o cclusions t o e nhance t he c ollected d ata t o improve t he a ccuracy o f HA-ODT.","PeriodicalId":384373,"journal":{"name":"Journal of Networking and Network Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134330574","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}

引用次数: 0

Consortium Blockchain based Reputation Incentive Mechanism for Recommendation System 基于联盟区块链的推荐系统声誉激励机制

Journal of Networking and Network Applications Pub Date : 1900-01-01 DOI: 10.33969/j-nana.2021.010305

Guo Sun, Tingting Zhao, Qingyi Ye, Chuntang Yu, Xia Feng

{"title":"Consortium Blockchain based Reputation Incentive Mechanism for Recommendation System","authors":"Guo Sun, Tingting Zhao, Qingyi Ye, Chuntang Yu, Xia Feng","doi":"10.33969/j-nana.2021.010305","DOIUrl":"https://doi.org/10.33969/j-nana.2021.010305","url":null,"abstract":"Recommendation systems have been widely used in many e-commerce services, but it is difficult to gather enough participants to supply their recommendations. Moreover, participants in the system may make malicious recommendations, which will affect the accuracy of recommendation results. In order to provide better recommendation service for users, incentive mechanisms are needed to attract more participants in recommendation and curb their malicious behaviors. In this paper, we propose a consortium blockchain based reputation incentive mechanism for recommendation systems(CRIM). Firstly, the monetary rewards are used to attract participants and motivate them to take part in the recommendation. Secondly, we design the incentive mechanism with reputation which is attached to the rewards. Honest participants will gain more rewards while malicious participants will be penalized. Meanwhile, we adopt the Stackelberg game to maximize the utility of participants, and prove that the mechanism can reach a unique Nash equilibrium. Thirdly, the decentralization and immutability of blockchain can guarantee the credibility and security of the stored data, thus ensuring the openness and transparency of the recommendation. Finally, we implement the system for education resources recommendation and conduct experiments, and the results demonstrate that our incentive mechanism is effective and has significant performance when compared with other incentive mechanisms.","PeriodicalId":384373,"journal":{"name":"Journal of Networking and Network Applications","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128425824","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}

引用次数: 1

Social-aware Content Uploading for Cooperative Device-to-Device Communications 协作设备对设备通信的社会意识内容上传

Journal of Networking and Network Applications Pub Date : 1900-01-01 DOI: 10.33969/j-nana.2022.020104

Xiaolan Liu, Bin Yang

引用次数: 0