2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)最新文献_第3页

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2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/cic50333.2020.00003

引用次数: 0

PriSE: Slenderized Privacy-Preserving Surveillance as an Edge Service 优点:作为边缘服务的细长隐私保护监视

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00024

Alem Fitwi, Yu Chen, Sencun Zhu

{"title":"PriSE: Slenderized Privacy-Preserving Surveillance as an Edge Service","authors":"Alem Fitwi, Yu Chen, Sencun Zhu","doi":"10.1109/CIC50333.2020.00024","DOIUrl":"https://doi.org/10.1109/CIC50333.2020.00024","url":null,"abstract":"With a myriad of edge cameras deployed in urban areas, many people are seriously concerned about the invasion of their privacy. The edge computing paradigm allows enforcing privacy-preserving measures at the point where the video frames are created. However, the resource constraints at the network edge make existing compute-intensive privacy-preserving solutions unaffordable. In this paper, we propose slenderized and efficient methods for Privacy-preserving Surveillance as an Edge service (PriSE) after investigating a spectrum of image-processing, image scrambling, and deep learning (DL) based mechanisms. At the edge cameras, the PriSE introduces an efficient and lightweight Reversible Chaotic Masking (ReCAM) scheme preceded by a simple foreground object detector. The scrambling scheme prevents an interception attack by ensuring end-to-end privacy. The simplified motion detector helps save bandwidth, processing time, and storage by discarding those frames that contain no foreground objects. On a fog/cloud server, the scrambling scheme is coupled with a robust window-detector to prevent peeping via windows and a multi-tasked convolutional neural network (MTCNN) based face-detector for the purpose of de-identification. The extensive experimental studies and comparative analysis show that the PriSE is able to efficiently detect foreground objects and scramble frames at the edge cameras, and detect and denature window and face objects at a fog/cloud server to ensure end-to-end communication privacy and anonymity, respectively. This is done just before the frames are sent to the viewing stations.","PeriodicalId":265435,"journal":{"name":"2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131355896","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

Hcpcs2Vec: Healthcare Procedure Embeddings for Medicare Fraud Prediction Hcpcs2Vec:医疗保险欺诈预测的医疗保健程序嵌入

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00026

Justin M. Johnson, T. Khoshgoftaar

{"title":"Hcpcs2Vec: Healthcare Procedure Embeddings for Medicare Fraud Prediction","authors":"Justin M. Johnson, T. Khoshgoftaar","doi":"10.1109/CIC50333.2020.00026","DOIUrl":"https://doi.org/10.1109/CIC50333.2020.00026","url":null,"abstract":"This study evaluates semantic healthcare procedure code embeddings on a Medicare fraud classification problem using publicly available big data. Traditionally, categorical Medicare features are one-hot encoded for the purpose of supervised learning. One-hot encoding thousands of unique procedure codes leads to high-dimensional vectors that increase model complexity and fail to capture the inherent relationships between codes. We address these shortcomings by representing procedure codes using low-rank continuous vectors that capture various dimensions of similarity. We leverage publicly available data from the Centers for Medicare and Medicaid Services, with more than 56 million claims records, and train Word2Vec models on sequences of co-occurring codes from the Healthcare Common Procedure Coding System (HCPCS). Continuous-bag-of-words and skip-gram embed-dings are trained using a range of embedding and window sizes. The proposed embeddings are empirically evaluated on a Medicare fraud classification problem using the Extreme Gradient Boosting learner. Results are compared to both one-hot encodings and pre-trained embeddings from related works using the area under the receiver operating characteristic curve and geometric mean metrics. Statistical tests are used to show that the proposed embeddings significantly outperform one-hot encodings with 95% confidence. In addition to our empirical analysis, we briefly evaluate the quality of the learned embeddings by exploring nearest neighbors in vector space. To the best of our knowledge, this is the first study to train and evaluate HCPCS procedure embeddings on big Medicare data.","PeriodicalId":265435,"journal":{"name":"2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122416321","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: 5

The 10 Research Topics in the Internet of Things 物联网十大研究课题

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00015

W. Zhang, Quan Z. Sheng, A. Mahmood, Dai Hoang Tran, Munazza Zaib, S. Hamad, Abdulwahab Aljubairy, A. Alhazmi, S. Sagar, Congbo Ma

引用次数: 20

Memory Abstraction and Optimization for Distributed Executors 分布式执行器的内存抽象与优化

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00019

S. Sahin, Ling Liu, Wenqi Cao, Qi Zhang, Juhyun Bae, Yanzhao Wu

{"title":"Memory Abstraction and Optimization for Distributed Executors","authors":"S. Sahin, Ling Liu, Wenqi Cao, Qi Zhang, Juhyun Bae, Yanzhao Wu","doi":"10.1109/CIC50333.2020.00019","DOIUrl":"https://doi.org/10.1109/CIC50333.2020.00019","url":null,"abstract":"This paper presents a suite of memory abstraction and optimization techniques for distributed executors, with the focus on showing the performance optimization opportunities for Spark executors, which are known to outperform Hadoop MapReduce executors by leveraging Resilient Distributed Datasets (RDDs), a fundamental core of Spark. This paper makes three original contributions. First, we show that applications on Spark experience large performance deterioration, when RDD is too large to fit in memory, causing unbalanced memory utilizations and premature spilling. Second, we develop a suite of techniques to guide the configuration of RDDs in Spark executors, aiming to optimize the performance of iterative ML workloads on Spark executors when their allocated memory is sufficient for RDD caching. Third, we design DAHI, a light-weight RDD optimizer. DAHI provides three enhancements to Spark: (i) using elastic executors, instead of fixed size JVM executors; (ii) supporting coarser grained tasks and large size RDDs by enabling partial RDD caching; and (iii) automatically leveraging remote memory for secondary RDD caching in the shortage of primary RDD caching on a local node. Extensive experiments on machine learning and graph processing benchmarks show that with DAHI, the performance of ML workloads and applications on Spark improves by up to 12.4x.","PeriodicalId":265435,"journal":{"name":"2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130446290","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

Crowdsharing Wireless Energy Services 众享无线能源服务

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-11-15 DOI: 10.1109/CIC50333.2020.00013

Abdallah Lakhdari, Amani Abusafia, A. Bouguettaya

引用次数: 15

Cyber Attacks on Smart Farming Infrastructure 对智能农业基础设施的网络攻击

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-10-15 DOI: 10.1109/CIC50333.2020.00025

Sina Sontowski, Maanak Gupta, Sai Sree Laya Chukkapalli, Mahmoud Abdelsalam, Sudip Mittal, A. Joshi, R. Sandhu

{"title":"Cyber Attacks on Smart Farming Infrastructure","authors":"Sina Sontowski, Maanak Gupta, Sai Sree Laya Chukkapalli, Mahmoud Abdelsalam, Sudip Mittal, A. Joshi, R. Sandhu","doi":"10.1109/CIC50333.2020.00025","DOIUrl":"https://doi.org/10.1109/CIC50333.2020.00025","url":null,"abstract":"Smart farming also known as precision agriculture is gaining more traction for its promising potential to fulfill increasing global food demand and supply. In a smart farm, technologies and connected devices are used in a variety of ways, from finding the real-time status of crops and soil moisture content to deploying drones to assist with tasks such as applying pesticide spray. However, the use of heterogeneous internet-connected devices has introduced numerous vulnerabilities within the smart farm ecosystem. Attackers can exploit these vulnerabilities to remotely control and disrupt data flowing from/to on-field sensors and autonomous vehicles like smart tractors and drones. This can cause devastating consequences especially during a high-risk time, such as harvesting, where live-monitoring is critical. In this paper, we demonstrate a Denial of Service (DoS) attack that can hinder the functionality of a smart farm by disrupting deployed on-field sensors. In particular, we discuss a Wi-Fi deauthentication attack that exploits IEEE 802.11 vulnerabilities, where the management frames are not encrypted. A MakerFocus ESP8266 Development Board WiFiDeauther Monster is used to detach the connected Raspberry Pi from the network and prevent sensor data from being sent to the remote cloud. Additionally, this attack was expanded to include the entire network, obstructing all devices from connecting to the network. To this end, we urge practitioners to be aware of current vulnerabilities when deploying smart farming ecosystems and encourage the cyber-security community to further investigate the domain-specific characteristics of smart farming.","PeriodicalId":265435,"journal":{"name":"2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132734557","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: 54

Message from the General Chairs and PC Chairs 来自普通主席和PC主席的信息

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2019-12-01 DOI: 10.1109/cogmi48466.2019.00005

引用次数: 0

Technical Program Committee 技术计划委员会

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2019-03-01 DOI: 10.1109/ssd.2019.8893186

A. Jamalipour, S. Aïssa, Pascal Lorenz

引用次数: 0