2019 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献_第2页

Homomorphic Encryption for Privacy-Preserving Genome Sequences Search 保护隐私的基因组序列搜索的同态加密

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-12 DOI: 10.1109/SMARTCOMP.2019.00021

Yuki Yamada, K. Rohloff, M. Oguchi

{"title":"Homomorphic Encryption for Privacy-Preserving Genome Sequences Search","authors":"Yuki Yamada, K. Rohloff, M. Oguchi","doi":"10.1109/SMARTCOMP.2019.00021","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00021","url":null,"abstract":"Genome sequence search is useful, for example, in clinical applications where a care provider needs to select a treatment option for a patient based on the exact kind of cancer the patient might have. Homomorphic encryption is a desirable technology to be used for this application because it is non-interactive. However, privacy-preserving genome sequence search using homomorphic encryption has been a practical challenge because of scalability issues driven by the depth of computations that need to be supported for privacy-preserving genome sequence search. In this paper, we build off of earlier privacy-preserving genome sequence search results to design, implement and compare two approaches to a client-server style system for privacy-preserving genome sequence search. There is a myriad of options and design trade-offs associated with the application of homomorphic encryption in this domain driven, for example, by choices in data encoding, scheme selection, and even encryption software library. We particularly focus on the use of the BGV and BFV homomorphic encryption schemes provided by the HElib and PALISADE open-source homomorphic encryption software libraries. Our results show that using the BFV-based approach in PALISADE provides optimal results for this application over our sample data.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114647478","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: 6

Online Distributed Analytics at the Edge with Multiple Service Grades 具有多个服务等级的边缘在线分布式分析

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-12 DOI: 10.1109/SMARTCOMP.2019.00028

Víctor Valls, Geeth de Mel, H. Kwon, L. Tassiulas

引用次数: 0

On the Feasibility of Attribute-Based Encryption on Constrained IoT Devices for Smart Systems 智能系统受限物联网设备基于属性加密的可行性研究

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-12 DOI: 10.1109/SMARTCOMP.2019.00057

Benedetto Girgenti, Pericle Perazzo, C. Vallati, F. Righetti, G. Dini, G. Anastasi

{"title":"On the Feasibility of Attribute-Based Encryption on Constrained IoT Devices for Smart Systems","authors":"Benedetto Girgenti, Pericle Perazzo, C. Vallati, F. Righetti, G. Dini, G. Anastasi","doi":"10.1109/SMARTCOMP.2019.00057","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00057","url":null,"abstract":"The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. Such IoT devices generate an uninterrupted flow of information that can be transmitted through an untrusted network and stored on an untrusted infrastructure. The latter raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a new type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e. smartphones or embedded systems, has been demonstrated in recent works. In this paper we assess the feasibility of the adoption of ABE in typical IoT constrained devices, characterized by limited capabilities in terms of computing, storage and power. Specifically, an implementation of three ABE schemes for ESP32, a low-cost popular platform to deploy IoT devices, is developed and evaluated in terms of encryption/decryption time and energy consumption. The performance evaluation shows that the adoption of ABE on constrained devices is feasible, although it has a cost that increases with the number of attributes. The analysis in particular highlights how ABE has a significant impact in the lifetime of battery-powered devices, which is impaired significantly when a high number of attributes is adopted.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132556708","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: 17

A Novel Smart System for Contaminants Detection and Recognition in Water 一种新型的水中污染物检测与识别智能系统

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-12 DOI: 10.1109/SMARTCOMP.2019.00051

M. Ferdinandi, M. Molinara, G. Cerro, L. Ferrigno, C. Marrocco, A. Bria, P. Meo, C. Bourelly, R. Simmarano

{"title":"A Novel Smart System for Contaminants Detection and Recognition in Water","authors":"M. Ferdinandi, M. Molinara, G. Cerro, L. Ferrigno, C. Marrocco, A. Bria, P. Meo, C. Bourelly, R. Simmarano","doi":"10.1109/SMARTCOMP.2019.00051","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00051","url":null,"abstract":"Nowadays water monitoring represents one of the most challenging global aims for the protection of people and environment health. In this paper we propose the application of an integrated system for the detection and recognition of contaminants in water. It is based on a two layer architecture: a sensing layer based on SENSIPLUS chip, and a data collection and classification layer, hereafter referred as SENSIPLUS Deep Machine (SDM). The SDM includes: a Micro Controller Unit (MCU), an optional host controller (e.g. laptop, smartphone, etc.) and different software components for data communication, analysis, and classification/regression based on machine learning techniques. Although the SDM classification/regression module can be potentially developed with any machine learning solution, in this paper we adopted an Artificial Neural Network with only one hidden layer to have a lightweight solution suitable to run (for inference) on ultra low power MCU. Aiming at further minimizing the network complexity, two alternative training sessions have been pursued: the first one using raw sensors' data and the second one applying a feature space dimensionality reduction through the Principal Component Analysis technique. Comparable and positive results (higher than 82% as average accuracy) have been obtained, confirming the validity and potentiality of the proposed system.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133254390","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

Identifying the Context of Hurricane Posts on Twitter using Wavelet Features 使用小波特征识别推特上飓风帖子的上下文

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-12 DOI: 10.1109/SMARTCOMP.2019.00072

A. Anam, A. Gangopadhyay, Nirmalya Roy

{"title":"Identifying the Context of Hurricane Posts on Twitter using Wavelet Features","authors":"A. Anam, A. Gangopadhyay, Nirmalya Roy","doi":"10.1109/SMARTCOMP.2019.00072","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00072","url":null,"abstract":"With the increase of natural disasters all over the world, we are in crucial need of innovative solutions with inexpensive implementations to assist the emergency response systems. Information collected through conventional sources (e.g., incident reports, 911 calls, physical volunteers, etc.) are proving to be insufficient [1]. Responsible organizations are now leaning towards research grounds that explore digital human connectivity and freely available sources of information. U.S. Geological Survey and Federal Emergency Management Agency (FEMA) introduced Critical Lifeline (CLL) s which identifies the most significant areas that require immediate attention in case of natural disasters. These organizations applied crowdsourcing by connecting digital volunteer networks to collect data on the critical lifelines from data sources including social media [3], [4], [5]. In the past couple of years, during some of the deadly hurricanes (e.g., Harvey, IRMA, Maria, Michael, Florence, etc.), people took on different social media platforms like never seen before, in search of help for rescue, shelter, and relief. Their posts reflect crisis updates and their real-time observations on the devastation that they witness. In this paper, we propose a methodology to build and analyze time-frequency features of words on social media to assist the volunteer networks in identifying the context before, during and after a natural disaster and distinguishing contexts connected to the critical lifelines. We employ Continuous Wavelet Transform to help create word features and propose two ways to reduce the dimensions which we use to create word clusters to identify themes of conversations associated with stages of a disaster and these lifelines. We compare two different methodologies of wavelet features and word clusters both qualitatively and quantitatively, to show that wavelet features can identify and separate context without using semantic information as inputs.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125752598","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

Distributed Communicating Neural Network Architecture for Smart Environments 面向智能环境的分布式通信神经网络架构

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-12 DOI: 10.1109/SMARTCOMP.2019.00058

Prince Abudu, A. Markham

{"title":"Distributed Communicating Neural Network Architecture for Smart Environments","authors":"Prince Abudu, A. Markham","doi":"10.1109/SMARTCOMP.2019.00058","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00058","url":null,"abstract":"The deployment of millions of embedded sensors plagued by resource constraints in sophisticated, complex and dynamic IoT smart environments continues to inspire the need to build novel architectures and models for automated, efficient inference and communication in distributed smart settings. In such settings, practical challenges related to energy efficiency, computational power and reliability, tedious design implementation, effective communication, optimal sampling and accurate event classification, prediction and detection exist. Sensors operating in smart environments must be capable of overcoming such challenges and enable scalable monitoring of dynamic phenomena while conducting real-time operations. The development of Machine Learning (ML) continues to motivate a new wave of innovative solutions that intermarry embedded sensors, IoT, and ML to enable various applications in smart environments. We propose a distributed communicating architecture based on Recurrent Neural Networks (RNNs) that can be instantiated on smart devices observing unique data and performing automated distributed inference via hidden-state communication. Our model uses a data-driven approach to collectively solve various distributed objectives, as evidenced by a series of systematic analyses we present. Although demonstrated on a small setup (2/3) nodes, this work sets out a new direction for automatically learning to communicate to solve tasks in distributed settings.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129077390","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: 2

Smartwatch Application for Horse Gaits Activity Recognition 马的步态活动识别智能手表应用

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-12 DOI: 10.1109/SMARTCOMP.2019.00080

Enrico Casella, A. R. Khamesi, S. Silvestri

{"title":"Smartwatch Application for Horse Gaits Activity Recognition","authors":"Enrico Casella, A. R. Khamesi, S. Silvestri","doi":"10.1109/SMARTCOMP.2019.00080","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00080","url":null,"abstract":"Activity recognition has been introduced as a means of detecting an action from a series of observations. Although in the literature, the terms \"activity recognition\" and \"human activity recognition\" are mostly used interchangeably, there exist several interesting applications for non-human subjects. In this work, we study animal activity recognition with special focus on horse gaits recognition. The on-body and unobtrusive system developed in this paper has several potential applications which can raise attention towards financial, emotional and veterinary health issues related to animals. Leveraging the pervasive use of smartwatches for activity tracking, we develop a smartwatch application to collect accelerometer data. The application is based on novel outlier detection and feature extraction techniques, in conjunction with state-of-the-art machine learning approaches. We perform real life experiments with two horses to evaluate the performances of our proposed system. To this aim, we place the monitoring device both on the horse saddle and the rider's wrist. The results show a high accuracy in both scenarios, which allows a seamless and unobtrusive use of our wearable device application by the rider. In addition, we study the effects of sliding window size and sampling frequency, providing useful insights for future research in horse gaits recognition.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"2507 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131284371","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

Overcoming the Lack of Labeled Data: Training Intrusion Detection Models Using Transfer Learning 克服标记数据的缺乏:利用迁移学习训练入侵检测模型

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-12 DOI: 10.1109/SMARTCOMP.2019.00031

Ankush Singla, E. Bertino, D. Verma

引用次数: 36

Unauthorized Parking Detection using Deep Networks at Real Time 使用深度网络实时检测未经授权的停车

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-12 DOI: 10.1109/SMARTCOMP.2019.00088

Weiling Chen, C. Yeo

引用次数: 5

Apis: Architecture for Federated Power Management api:联邦电源管理的体系结构

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-12 DOI: 10.1109/SMARTCOMP.2019.00046

Adam Prey, Jiannan Zhai, Chancey Kelley, J. Hallstrom

引用次数: 0