2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)最新文献

{"title":"PADS: Privacy-Preserving Auction Design for Allocating Dynamically Priced Cloud Resources","authors":"Jinlai Xu, Balaji Palanisamy, Y. Tang, S.D. Madhu Kumar","doi":"10.1109/CIC.2017.00023","DOIUrl":"https://doi.org/10.1109/CIC.2017.00023","url":null,"abstract":"With the rapid growth of Cloud Computing technologies, enterprises are increasingly deploying their services in the Cloud. Dynamically priced cloud resources such as the Amazon EC2 Spot Instance provides an efficient mechanism for cloud service providers to trade resources with potential buyers using an auction mechanism. With the dynamically priced cloud resource markets, cloud consumers can buy resources at a significantly lower cost than statically priced cloud resources such as the on-demand instances in Amazon EC2. While dynamically priced cloud resources enable to maximize datacenter resource utilization and minimize cost for the consumers, unfortunately, such auction mechanisms achieve these benefits only at a cost significant of private information leakage. In an auction-based mechanism, the private information includes information on the demands of the consumers that can lead an attacker to understand the current computing requirements of the consumers and perhaps even allow the inference of the workload patterns of the consumers. In this paper, we propose PADS, a strategy-proof differentially private auction mechanism that allows cloud providers to privately trade resources with cloud consumers in such a way that individual bidding information of the cloud consumers is not exposed by the auction mechanism. We demonstrate that PADS achieves differential privacy and approximate truthfulness guarantees while maintaining good performance in terms of revenue gains and allocation efficiency. We evaluate PADS through extensive simulation experiments that demonstrate that in comparison to traditional auction mechanisms, PADS achieves relatively high revenues for cloud providers while guaranteeing the privacy of the participating consumers.","PeriodicalId":156843,"journal":{"name":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130733765","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}

{"title":"Exploring the Effectiveness of Twitter at Polling the United States 2016 Presidential Election","authors":"Brian Heredia, Joseph D. Prusa, T. Khoshgoftaar","doi":"10.1109/CIC.2017.00045","DOIUrl":"https://doi.org/10.1109/CIC.2017.00045","url":null,"abstract":"Tweets are frequently used to express opinions, specifically when the topic of choice is polarizing, as it is in politics. With many variables effecting the choice of vote, the most effective method of determining election outcome is through public opinion polling. We seek to determine whether Twitter can be an effective polling method for the 2016 United States general election. To this aim, we create a dataset consisting of approximately 3 million tweets ranging from September 22nd to November 8th related to either Donald Trump or Hillary Clinton. We incorporate two approaches in polling voter opinion for election outcomes: tweet volume and sentiment. Our data is labeled via a convolutional neural network trained on the sentiment140 dataset. To determine whether Twitter is an indicator of election outcome, we compare our results to three polls conducted by various reputable sources during the 13 days before the election. Our results show that when using tweet sentiment, we obtain similar margins to polls conducted during the election period and come close to the actual popular vote outcome.","PeriodicalId":156843,"journal":{"name":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","volume":"135 30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114373633","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}

{"title":"Source Identification for Printed Documents","authors":"Min-Jen Tsai, Imam Yuadi, Yu-Han Tao, Jin-Sheng Yin","doi":"10.1109/CIC.2017.00019","DOIUrl":"https://doi.org/10.1109/CIC.2017.00019","url":null,"abstract":"Technological advances in digitization with a variety of image manipulation techniques enable the creation of printed documents illegally. Correspondingly, many researchers conduct studies in determining whether the document printed counterfeit or original. This study examines the several statistical feature sets from Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, Wiener filter, Gabor filter, Haralick and fractal filters to identify text and image document by using support vector machine (SVM) and decision fusion of feature selection. The average experimental results achieves that the image document is higher identification rate than text document. In summary, the proposed method outperforms the previous researches and it is a promising technique that can be implemented in real forensics for printed documents.","PeriodicalId":156843,"journal":{"name":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121740523","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}

{"title":"USNB: Enabling Universal Online Social Interactions","authors":"Rafael Angarita, N. Georgantas, V. Issarny","doi":"10.1109/CIC.2017.00048","DOIUrl":"https://doi.org/10.1109/CIC.2017.00048","url":null,"abstract":"Online social network services (OSNSs) have become an integral part of our daily lives. At the same time, the aggressive market competition has led to the emergence of multiple competing siloed OSNSs that cannot interoperate. As a consequence, people face the burden of creating and managing multiple OSNS accounts and learning how to use them to stay connected. This paper is concerned with relieving users from such a burden by enabling universal online social interactions. The contributions of this paper span: (1) a model of the universal social network bus (USNB) for OSNS interoperability; (2) a prototype for universal online social interactions that builds upon the proposed model; and (3) a preliminary experimental evaluation involving 50 participants. Results show that people are positive about the solution as they are able to reach out a larger community of users independently of the OSNSs they use.","PeriodicalId":156843,"journal":{"name":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115018620","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}

{"title":"Quantifying Content Polarization on Twitter","authors":"Muhe Yang, Xidao Wen, Y. Lin, Lingjia Deng","doi":"10.1109/CIC.2017.00047","DOIUrl":"https://doi.org/10.1109/CIC.2017.00047","url":null,"abstract":"Social media like Facebook and Twitter have become major battlegrounds, with increasingly polarized content disseminated to people having different interests and ideologies. This work examines the extent of content polarization during the 2016 U.S. presidential election, from a unique, \"content\" perspective. We propose a new approach to quantify the polarization of content semantics by leveraging the word embedding representation and clustering metrics. We then propose an evaluation framework to verify the proposed quantitative measurement using a stance classification task. Based on the results, we further explore the extent of content polarization during the election period and how it changed across time, geography, and different types of users. This work contributes to understanding the online \"echo chamber\" phenomenon based on user-generated content.","PeriodicalId":156843,"journal":{"name":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115267387","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}

{"title":"Security Failure Trends of Cloud Computing","authors":"Zachariah Pabi Gariba, J. A. V. D. Poll","doi":"10.1109/CIC.2017.00041","DOIUrl":"https://doi.org/10.1109/CIC.2017.00041","url":null,"abstract":"Cloud computing technology is causing a shift in Information Communication Technology usage by transforming the approaches businesses employ information technology services. This benefits computing usage by allowing users to share hardware resources via multiplexing of virtual machines which are the basic units of cloud computing. Small and new businesses which often lack good financial standing but desire to use the cloud, need not own these resources, but only pay for their use at a reduced price. This alleviates the cost of technological maintenance and operation. However, the services provided by third-party cloud service providers involve inherent security threats. The relocation of corporate resources to an external administrative jurisdiction escalates security concerns such as trust, assurance and transparency. There may be devastating effects to clients, should their financial transactions and corporate information be compromised as a result of a service provider's security challenges. This research employs a quantitative case study to analyse the security challenges of popular cloud computing services existing within the past four years. An overview of security vulnerabilities in cloud computing is presented; a preliminary cloud security framework is developed and the role of computing formalisms is investigated.","PeriodicalId":156843,"journal":{"name":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126058420","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}

{"title":"An Access Control Framework for Cloud-Enabled Wearable Internet of Things","authors":"Smriti Bhatt, Farhan Patwa, R. Sandhu","doi":"10.1109/CIC.2017.00050","DOIUrl":"https://doi.org/10.1109/CIC.2017.00050","url":null,"abstract":"Internet of Things (IoT) has become a pervasive and diverse concept in recent years. IoT applications and services have given rise to a number of sub-fields in the IoT space. Wearable technology, with its particular set of characteristics and application domains, has formed a rapidly growing sub-field of IoT, viz., Wearable Internet of Things (WIoT). While numerous wearable devices are available in the market today, security and privacy are key factors for wide adoption of WIoT. Wearable devices are resource constrained by nature with limited storage, power, and computation. A Cloud-Enabled IoT (CEIoT) architecture, a dominant paradigm currently shaping the industry and suggested by many researchers, needs to be adopted for WIoT. In this paper, we develop an access control framework for cloud-enabled WIoT (CEWIoT) based on the Access Control Oriented (ACO) architecture recently developed for CEIoT in general. We first enhance the ACO architecture from the perspective of WIoT by adding an Object Abstraction Layer, and then develop our framework based on interactions between different layers of this enhanced ACO architecture. We present a general classification and taxonomy of IoT devices, along with brief introduction to various application domains of IoT and WIoT. We then present a remote health and fitness monitoring use case to illustrate different access control aspects of our framework and outline its possible enforcement in a commercial CEIoT platform, viz., AWS IoT. Finally, we discuss the objectives of our access control framework and relevant open problems.","PeriodicalId":156843,"journal":{"name":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127241032","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}

{"title":"From Myths to Norms: Demystifying Data Mining Models with Instance-Based Transparency","authors":"Y. Alufaisan, Yan Zhou, Murat Kantarcioglu, B. Thuraisingham","doi":"10.1109/CIC.2017.00042","DOIUrl":"https://doi.org/10.1109/CIC.2017.00042","url":null,"abstract":"The desire of moving from data to intelligence has become a trend that pushes the world we live in today fast forward. Machine learning and data mining techniques are being used as important tools to unlock the wealth of voluminous amounts of data owned by organizations. Despite the existing effort of explaining their underlying machinery in layman's terms, data mining models and their output remain as esoteric, discipline-based black boxes-viable only to experts with years of training and development experiences. As data mining techniques gain growing popularity in the real world, the ability to understand their intelligent decision-making artifacts has become increasingly important and critical, especially in areas such as criminal justice and law enforcement where transparency of decision-making is vital for ensuring fairness, justice, and equality. In this paper, we present a transparency model to help unmask the incomprehensible reasoning many data mining techniques are deservedly taking the blame for. Our transparency model substitutes a comprehensible, rule-based counterpart for the complex, black-box output of any data mining technique using a novel rule selection technique. The rule-based substitute explains the decision made for each instance with a tiny set of rules, resulting in a significant reduction in model complexity. Besides model simplicity and comprehensibility, we also assess the quality of our rule set by measuring its similarity to the output of the original data mining algorithm. Furthermore, we compute its accuracy on unseen test data as a complementary assessment criteria. We empirically demonstrate the effectiveness of our transparency model by experimenting on eight real datasets that deal with predicting important personal attributes ranging from credit worthiness to criminal recidivism. Our transparency model demonstrates a high degree of consistency with the original data mining algorithms in nearly all cases. We also compare our results to one of the state-of-the-art transparency models-LIME, and show that our transparency model outperforms LIME 84% of the time.","PeriodicalId":156843,"journal":{"name":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132214529","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}

{"title":"A Socio-Technical Perspective in Support of Information Sharing for Diverse Teams in Today's Workplace","authors":"Laura C. Anderson, C. Kieliszewski","doi":"10.1109/CIC.2017.00059","DOIUrl":"https://doi.org/10.1109/CIC.2017.00059","url":null,"abstract":"Work today is accomplished by people in teams that are increasingly distributed, collaborative, and heterogeneous. One or more dimensions of team heterogeneity can be experienced by collaborators as distance. Geography, time zone, discipline, and organizational factors such as role and project tenure are of particular interest. This diversity, when experienced as distance, provides challenges to the social fabric of teams, and calls for additional technological development to provide information sharing tools that are capable of recognizing and addressing these gaps without adding additional burden on the individual or team. We discuss the distances, information sharing challenges, and strategies to place the human at the center, with a focus on tool and technology simplification, personalization and consolidation.","PeriodicalId":156843,"journal":{"name":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121321743","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}

{"title":"A Risk-Aware Access Control Framework for Cyber-Physical Systems","authors":"Nuray Baltaci Akhuseyinoglu, J. Joshi","doi":"10.1109/CIC.2017.00052","DOIUrl":"https://doi.org/10.1109/CIC.2017.00052","url":null,"abstract":"Cyber-physical systems (CPS) integrate cyber components into physical processes. This integration enhances the capabilities of physical systems by incorporating intelligence into objects and services. On the other hand, integration of cyber and physical components and interaction between them introduce new security threats. Since CPSs are mostly safety-critical systems, data stored and communicated in them are highly critical. Hence, there is an inevitable need for protecting the data and resources against unauthorized accesses. In this paper, we propose an access control (AC) framework to address CPS related security issues. The proposed framework consists of two parts: a cyber-physical access control model (CPAC) and a generalized action generation model (GAGM). CPAC utilizes an attribute based approach and extends it with cyber-physical components and cyber-physical interactions. GAGM is used to augment enforcement of authorization policies. We present formal representations of CPAC and GAGM, and provide a sample scenario for a medical CPS. We propose an algorithm for enforcing authorization policies.","PeriodicalId":156843,"journal":{"name":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123240889","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}