2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)最新文献_第5页

TagBreathe: Monitor Breathing with Commodity RFID Systems TagBreathe:用商品RFID系统监测呼吸

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2017-06-05 DOI: 10.1109/ICDCS.2017.76

Yuxiao Hou, Yanwen Wang, Yuanqing Zheng

{"title":"TagBreathe: Monitor Breathing with Commodity RFID Systems","authors":"Yuxiao Hou, Yanwen Wang, Yuanqing Zheng","doi":"10.1109/ICDCS.2017.76","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.76","url":null,"abstract":"Breath monitoring helps assess the general personal health and gives clues to chronic diseases. Yet current breath monitoring technologies are inconvenient and intrusive. For instance, typical breath monitoring devices need to attach nasal probes or chest bands to users. Wireless sensing technologies have been applied to monitor breathing using radio waves without physical contact. Those wireless sensing technologies however require customized radios which are not readily available. More importantly, due to interference, such technologies do not work well with multiple users. With multiple users in presence, the detection accuracy of existing systems decreases dramatically. In this paper, we propose to monitor users' breathing using commercial-off-the-shelf (COTS) RFID systems. In our system, passive lightweight RFID tags are attached to users' clothes and backscatter radio waves, and commodity RFID readers report low level data (e.g., phase values). We track periodic body movement due to inhaling and exhaling by analyzing the low level data reported by commodity readers. To enhance the measurement robustness, we synthesize data streams from an array of multiple tags to improve the monitoring accuracy. Our design follows the standard EPC protocol which arbitrates collisions in the presence of multiple tags. We implement a prototype the breath monitoring system with commodity RFID systems. The experiment results show that the prototype system can simultaneously monitor breathing with high accuracy even with the presence of multiple users.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133283469","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: 85

Learning Privacy Habits of PDS Owners 学习PDS用户的隐私习惯

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2017-06-05 DOI: 10.1109/ICDCS.2017.65

B. C. Singh, B. Carminati, E. Ferrari

引用次数: 10

Machine to Machine Trust in Smart Cities 智慧城市中的机器对机器信任

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2017-06-05 DOI: 10.1109/ICDCS.2017.329

M. Loper, B. Swenson

引用次数: 9

Consensus Robustness and Transaction De-Anonymization in the Ripple Currency Exchange System 瑞波币交换系统的共识鲁棒性与交易去匿名化

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2017-06-05 DOI: 10.1109/ICDCS.2017.52

Adriano Di Luzio, A. Mei, Julinda Stefa

{"title":"Consensus Robustness and Transaction De-Anonymization in the Ripple Currency Exchange System","authors":"Adriano Di Luzio, A. Mei, Julinda Stefa","doi":"10.1109/ICDCS.2017.52","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.52","url":null,"abstract":"Distributed financial systems are radically changing the way we do business and spend our money. Ripple, in particular, is unique in its kind. It is built on consensus and trust among its users and it allows to exchange both fiat currencies and goods over its network. It does so by storing the accounts of its users, their balances, and all the transactions in a distributed ledger, publicly accessible. In this paper we perform an in-depth study of the Ripple exchange system and its public distributed ledger. We analyze payments, the structure of payment paths, and the role of the entities in the system such as Gateways (the equivalent of banks) and Market Makers. We also analyze the internal stream of events and show that Ripple relies on a surprisingly small number of active validators, raising concerns on the actual robustness and fairness of the system. Moreover, we consider the degree of anonymity that Ripple is able to guarantee. By examining the first three years of Ripple history (more than 500 GB worth of data), we show that even approximate information on a single payment can uncover, with incredible accuracy, the entire financial life of the user. For example, anyone who overhears our order of a Latte at our favourite bar can easily get complete and unlimited access to our balance, our previous and future payments, our monthly income, as well as critical information about the places where we shop and the people we trust.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121981638","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: 10

Kalis — A System for Knowledge-Driven Adaptable Intrusion Detection for the Internet of Things Kalis——面向物联网的知识驱动自适应入侵检测系统

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2017-06-05 DOI: 10.1109/ICDCS.2017.104

Daniele Midi, Antonino Rullo, Anand Mudgerikar, E. Bertino

引用次数: 131

Multi-resource Load Balancing for Virtual Network Functions 虚拟网络功能的多资源负载均衡

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2017-06-05 DOI: 10.1109/ICDCS.2017.233

Tao Wang, Hong Xu, Fangming Liu

{"title":"Multi-resource Load Balancing for Virtual Network Functions","authors":"Tao Wang, Hong Xu, Fangming Liu","doi":"10.1109/ICDCS.2017.233","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.233","url":null,"abstract":"Middleboxes are widely deployed to perform various network functions to ensure security and improve performance. The recent trend of Network Function Virtualization (NFV) makes it easy for operators to deploy software implementations of these network functions on commodity servers. However, virtual network functions consume different amounts of resources when processing packets. Thus a multi-resource load balancing (MRLB) mechanism is needed to efficiently utilize server resources. MRLB problem in the context of NFV is fundamentally different from multi-resource allocation problems, as well as traditional single-resource load balancing and multi-resource load balancing problems in task scheduling. In this paper, we tackle the MRLB problem in NFV by first proposing dominant load-the load of the most stressed resource on a server-as the load balancing metric. We then formulate the MRLB problem as an optimization to minimize the maximum dominant load of all NFV servers given the demand. Based on proximal Jacobian ADMM, we propose an efficient algorithm to solve the problem in large scale settings. Through extensive trace-driven simulations and prototype experiments on a testbed, we show that our MRLB algorithm with dominant load performs significantly better and faster than benchmarking algorithms.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116039194","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: 46

Virtualized Network Coding Functions on the Internet 虚拟化网络编码在Internet上的功能

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2017-06-05 DOI: 10.1109/ICDCS.2017.95

Linquan Zhang, Shangqi Lai, Chuan Wu, Zongpeng Li, Chuanxiong Guo

{"title":"Virtualized Network Coding Functions on the Internet","authors":"Linquan Zhang, Shangqi Lai, Chuan Wu, Zongpeng Li, Chuanxiong Guo","doi":"10.1109/ICDCS.2017.95","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.95","url":null,"abstract":"Network coding is a fundamental tool that enables higher network capacity and lower complexity in routing algorithms, by encouraging the mixing of information flows in the middle of a network. Implementing network coding in the core Internet is subject to practical concerns, since Internet routers are often overwhelmed by packet forwarding tasks, leaving little processing capacity for coding operations. Inspired by the recent paradigm of network function virtualization, we propose implementing network coding as a new network function, and deploying such coding functions in geo-distributed cloud data centers, to practically enable network coding on the Internet. We target multicast sessions (including unicast flows as special cases), strategically deploy relay nodes (network coding functions) in selected data centers between senders and receivers, and embrace high bandwidth efficiency brought by network coding with dynamic coding function deployment. We design and implement the network coding function on typical virtual machines, featuring efficient packet processing. We propose an efficient algorithm for coding function deployment, scaling in and out, in the presence of system dynamics. Real-world implementation on Amazon EC2 and Linode demonstrates significant throughput improvement and higher robustness of multicast via coding functions as well as efficiency of the dynamic deployment and scaling algorithm.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125665172","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

On Directional Neighbor Discovery in mmWave Networks 毫米波网络中的定向邻居发现

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2017-06-05 DOI: 10.1109/ICDCS.2017.229

Yu Wang, S. Mao, T. Rappaport

引用次数: 33

Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices 云、边缘和终端设备上的分布式深度神经网络

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2017-06-05 DOI: 10.1109/ICDCS.2017.226

Surat Teerapittayanon, Bradley McDanel, H. T. Kung

{"title":"Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices","authors":"Surat Teerapittayanon, Bradley McDanel, H. T. Kung","doi":"10.1109/ICDCS.2017.226","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.226","url":null,"abstract":"We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep neural network (DNN) in the cloud, a DDNN also allows fast and localized inference using shallow portions of the neural network at the edge and end devices. When supported by a scalable distributed computing hierarchy, a DDNN can scale up in neural network size and scale out in geographical span. Due to its distributed nature, DDNNs enhance sensor fusion, system fault tolerance and data privacy for DNN applications. In implementing a DDNN, we map sections of a DNN onto a distributed computing hierarchy. By jointly training these sections, we minimize communication and resource usage for devices and maximize usefulness of extracted features which are utilized in the cloud. The resulting system has built-in support for automatic sensor fusion and fault tolerance. As a proof of concept, we show a DDNN can exploit geographical diversity of sensors to improve object recognition accuracy and reduce communication cost. In our experiment, compared with the traditional method of offloading raw sensor data to be processed in the cloud, DDNN locally processes most sensor data on end devices while achieving high accuracy and is able to reduce the communication cost by a factor of over 20x.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131079293","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: 618

Privacy Preserving User-Based Recommender System 隐私保护基于用户的推荐系统

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2017-06-05 DOI: 10.1109/ICDCS.2017.248

S. Badsha, X. Yi, I. Khalil, E. Bertino

{"title":"Privacy Preserving User-Based Recommender System","authors":"S. Badsha, X. Yi, I. Khalil, E. Bertino","doi":"10.1109/ICDCS.2017.248","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.248","url":null,"abstract":"With the rapid development of the social networks, Collaborative Filtering (CF)-based recommender systems have been increasingly prevalent and become widely accepted by users. The CF-based techniques generate recommendations by collecting privacy sensitive data from users. Usually, the users are sensitive to disclosure of personal information and, consequently, there are unavoidable security concerns since private information can be easily misused by malicious third parties. In order to protect against breaches of personal information, it is necessary to obfuscate user information by means of an efficient encryption technique while simultaneously generating the recommendation by making true information inaccessible to service providers. Therefore, we propose a privacy preserving user-based CF technique based on homomorphic encryption, which is capable of determining similarities among users followed by generating recommendations without revealing any private information. We introduce different semi-honest parties to preserve privacy and to carry out intermediate computations for generating recommendations. We implement our method on publicly available datasets and show that our method is practical as well as achieves high level of security for users without compromising the recommendation accuracy.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127244892","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: 42