2019 IEEE Cloud Summit最新文献

Analytical Evaluation of k–Anonymity Algorithm and Epsilon-Differential Privacy Mechanism in Cloud Computing Environment 云计算环境下k -匿名算法和epsilon -差分隐私机制的分析评价

2019 IEEE Cloud Summit Pub Date : 2019-08-01 DOI: 10.1109/CloudSummit47114.2019.00023

A. Razaque, H. Yiming, Yan Shilin

{"title":"Analytical Evaluation of k–Anonymity Algorithm and Epsilon-Differential Privacy Mechanism in Cloud Computing Environment","authors":"A. Razaque, H. Yiming, Yan Shilin","doi":"10.1109/CloudSummit47114.2019.00023","DOIUrl":"https://doi.org/10.1109/CloudSummit47114.2019.00023","url":null,"abstract":"Expected and unexpected risks in cloud computing, which included data security, data segregation, and the lack of control and knowledge, have led to some dilemmas in several fields. Among all of these dilemmas, the privacy problem is even more paramount, which has largely constrained the prevalence and development of cloud computing. There are several privacy protection algorithms proposed nowadays, which generally include two categories, Anonymity algorithm, and differential privacy mechanism. Since many types of research have already focused on the efficiency of the algorithms, few of them emphasized the different orientation and demerits between the two algorithms. Motivated by this emerging research challenge, we have conducted a comprehensive survey on the two popular privacy protection algorithms, namely K-Anonymity Algorithm and Differential Privacy Algorithm. Based on their principles, implementations, and algorithm orientations, we have done the evaluations of these two algorithms. Several expectations and comparisons are also conducted based on the current cloud computing privacy environment and its future requirements.","PeriodicalId":255752,"journal":{"name":"2019 IEEE Cloud Summit","volume":"4 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121003030","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: 3

Multi-Model Deep Learning for Cloud Resources Prediction to Support Proactive Workflow Adaptation 基于多模型深度学习的云资源预测支持主动工作流适应

2019 IEEE Cloud Summit Pub Date : 2019-08-01 DOI: 10.1109/CloudSummit47114.2019.00019

H. El-Kassabi, M. Serhani, S. Bouktif, A. Benharref

{"title":"Multi-Model Deep Learning for Cloud Resources Prediction to Support Proactive Workflow Adaptation","authors":"H. El-Kassabi, M. Serhani, S. Bouktif, A. Benharref","doi":"10.1109/CloudSummit47114.2019.00019","DOIUrl":"https://doi.org/10.1109/CloudSummit47114.2019.00019","url":null,"abstract":"Scientific workflows are complex, resource intensive, dynamic in nature and require elastic cloud resources. To support these requirements, cloud resources' prediction schemes forecast resource scarcity and therefore support proactive workflow adaptation. In this paper, we propose a proactive workflow adaptation approach supported by a Deep Learning based prediction of cloud resources' usage. The model uses an algorithm to evaluate and privilege the most appropriate prediction model for resource utilization violations for each task of the workflow. Then, it recommends the proper adaptation actions to maintain the Quality of Service (QoS) for the entire workflow. Runtime monitoring of cloud resources data is continuously fed into Machine Learning models including GRU, LSTM, and Bi-directional LSTM for predicting the future task resource utilization values. The algorithm evaluates the resources' prediction using a number of metrics, such as RMSE, MAE, and MAPE. The prediction model achieving the highest accuracy is selected to determine the needed cloud resources. We conducted a series of experiments to evaluate our approach and the results demonstrate that the proposed Multi-Model predicts properly the cloud resource usage as well as suggesting their adaptation actions to guarantee the required workflow QoS.","PeriodicalId":255752,"journal":{"name":"2019 IEEE Cloud Summit","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128984869","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: 3

Poster Abstract: The Impact of DDoS Attack on Docker Containers Compared to Virtual Machines 摘要:与虚拟机相比，DDoS攻击对Docker容器的影响

2019 IEEE Cloud Summit Pub Date : 2019-08-01 DOI: 10.1109/CloudSummit47114.2019.00026

Jeffrey M. Galloway, Austin White, Patrick O'Boyle, Sierra Wyllie

引用次数: 2

DOCMA: A Decentralized Orchestrator for Containerized Microservice Applications DOCMA:容器化微服务应用的去中心化编排器

2019 IEEE Cloud Summit Pub Date : 2019-08-01 DOI: 10.1109/CloudSummit47114.2019.00014

Lara Lorna Jiménez, O. Schelén

引用次数: 9

On the Challenges of Executing Bioinformatic Tools Using a Distributed Cloud Platform 使用分布式云平台执行生物信息学工具的挑战

2019 IEEE Cloud Summit Pub Date : 2019-08-01 DOI: 10.1109/CloudSummit47114.2019.00012

Jeffrey M. Galloway, Jamie Thomas, Bailey Henson, A. Kirby, M. Armstrong

引用次数: 0

[Title page i] [标题页i]

2019 IEEE Cloud Summit Pub Date : 2019-08-01 DOI: 10.1109/cloudsummit47114.2019.00001

引用次数: 0

A Privacy-Preserving Framework for Collaborative Association Rule Mining in Cloud 云环境下协同关联规则挖掘的隐私保护框架

2019 IEEE Cloud Summit Pub Date : 2019-08-01 DOI: 10.1109/CloudSummit47114.2019.00025

B. Samanthula, Salha Al-Behairi, Boxiang Dong

{"title":"A Privacy-Preserving Framework for Collaborative Association Rule Mining in Cloud","authors":"B. Samanthula, Salha Al-Behairi, Boxiang Dong","doi":"10.1109/CloudSummit47114.2019.00025","DOIUrl":"https://doi.org/10.1109/CloudSummit47114.2019.00025","url":null,"abstract":"Collaborative Data Mining facilitates multiple organizations to integrate their datasets and extract useful knowledge from their joint datasets for mutual benefits. The knowledge extracted in this manner is found to be superior to the knowledge extracted locally from a single organization's dataset. With the rapid development of outsourcing, there is a growing interest for organizations to outsource their data mining tasks to a cloud environment to effectively address their economic and performance demands. However, due to privacy concerns and stringent compliance regulations, organizations do not want to share their private datasets neither with the cloud nor with other participating organizations. In this paper, we address the problem of outsourcing association rule mining task to a federated cloud environment in a privacy-preserving manner. Specifically, we propose a privacy-preserving framework that allows a set of users, each with a private dataset, to outsource their encrypted databases and the cloud returns the association rules extracted from the aggregated encrypted databases to the participating users. Our proposed solution ensures the confidentiality of the outsourced data and also minimizes the users' participation during the association rule mining process. Additionally, we show that the proposed solution is secure under the standard semihonest model and demonstrate its practicality.","PeriodicalId":255752,"journal":{"name":"2019 IEEE Cloud Summit","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115679839","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: 3

Comparison of Privacy-Preserving Models Based on a Third-Party Auditor in Cloud Computing 云计算中基于第三方审计的隐私保护模型比较

2019 IEEE Cloud Summit Pub Date : 2019-08-01 DOI: 10.1109/CloudSummit47114.2019.00020

J. Dichter, Navarun Gupta

引用次数: 2

Experimental Study on the Detectability of Man-in-the-Middle Attacks for Cloud Applications 云应用中间人攻击可检测性实验研究

2019 IEEE Cloud Summit Pub Date : 2019-08-01 DOI: 10.1109/CloudSummit47114.2019.00015

Cheng-Yu Cheng, E. Colbert, Hang Liu

{"title":"Experimental Study on the Detectability of Man-in-the-Middle Attacks for Cloud Applications","authors":"Cheng-Yu Cheng, E. Colbert, Hang Liu","doi":"10.1109/CloudSummit47114.2019.00015","DOIUrl":"https://doi.org/10.1109/CloudSummit47114.2019.00015","url":null,"abstract":"Man-in-the-Middle (MITM) attacks can significantly compromise the security of the Internet and cloud computing applications, where an attacker intercepts the packets transmitted between the clients and servers over the network to steal confidential information and/or change the packets. It is essential and challenging to detect MITM attacks. In this paper, we build a virtual network testbed to emulate a real-world cloud environment and study the detection of MITM attacks. We consider an MITM detection approach that utilizes network packet analysis and machine learning techniques to measure the changes in the packet Round-Trip Time (RTT) between a client and a server. Specifically, we use the machine learning algorithms in TensorFlow to analyze the RTT data collected on the testbed to determine the detectability of MITM attacks. If the attacker's link speed is much higher than the client's link speed in an access network, e.g. an attacker connecting the network through a wired Ethernet and a normal client connecting the network through a wireless link, it would be difficult to discern the RTT difference with and without the MITM attacks. We are able to deduce a threshold below which the MITM attacks can be detected based on the RTT difference with a certain accuracy. Our experiments show the detectability accuracy becomes lower.","PeriodicalId":255752,"journal":{"name":"2019 IEEE Cloud Summit","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122209550","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: 8

Cloud Computing: History and Overview 云计算:历史和概述

2019 IEEE Cloud Summit Pub Date : 2019-08-01 DOI: 10.1109/CloudSummit47114.2019.00007

Jayachander Surbiryala, Chunming Rong

引用次数: 27