Proceedings of the Third Workshop on Data: Acquisition To Analysis最新文献

Designing privacy preserving data sharing middleware for internet of things 保护隐私的物联网数据共享中间件设计

Proceedings of the Third Workshop on Data: Acquisition To Analysis Pub Date : 2020-11-16 DOI: 10.1145/3419016.3431484

Sameera Ghayyur, Primal Pappachan, Guoxi Wang, S. Mehrotra, N. Venkatasubramanian

{"title":"Designing privacy preserving data sharing middleware for internet of things","authors":"Sameera Ghayyur, Primal Pappachan, Guoxi Wang, S. Mehrotra, N. Venkatasubramanian","doi":"10.1145/3419016.3431484","DOIUrl":"https://doi.org/10.1145/3419016.3431484","url":null,"abstract":"The rise of low-cost Internet of Things (IoT) sensing and communication capabilities has given rise to a range of new smart services that rely on heterogeneous data from devices embedded in our everyday lives. The provision of such IoT services relies on environmental or user data from other data controllers (e.g. network provider, water agency, building management). Recent privacy regulations such as the European General Data Protection Requirement (GDPR) and California Consumer Privacy Act (CCPA) have made it mandatory for data controllers to perform enhanced processing of the shared data with appropriate privacy-preserving mechanisms before release to service providers. To facilitate this, we propose PE-IoT, a system for orchestrating privacy-enhanced data flows that (a) provides users (data subjects) with capabilities to opt-in/opt-out in the data that is shared with the service providers and (b) enable data controllers to invoke a range of Privacy Enhancing Technologies (PETs) such as anonymization, randomization, and perturbation to transform data streams into their privacy preserving counterparts. PE-IoT is based on a new model for privacy compliant data sharing and we describe the design and architecture of the PE-IoT system based on this model.","PeriodicalId":177625,"journal":{"name":"Proceedings of the Third Workshop on Data: Acquisition To Analysis","volume":"37 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132360350","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

Person tracking and identification using cameras and wi-fi channel state information (CSI) from smartphones: dataset 使用来自智能手机的摄像头和wi-fi通道状态信息(CSI)进行人员跟踪和身份识别:数据集

Proceedings of the Third Workshop on Data: Acquisition To Analysis Pub Date : 2020-11-16 DOI: 10.1145/3419016.3431488

Shiwei Fang, Sirajum Munir, S. Nirjon

{"title":"Person tracking and identification using cameras and wi-fi channel state information (CSI) from smartphones: dataset","authors":"Shiwei Fang, Sirajum Munir, S. Nirjon","doi":"10.1145/3419016.3431488","DOIUrl":"https://doi.org/10.1145/3419016.3431488","url":null,"abstract":"Human sensing, motion trajectory estimation, and identification are crucial to applications such as customer analysis, public safety, smart homes and cities, and access control. In the wake of the global COVID-19 pandemic, the ability to perform contact tracing effectively is vital to limit the spread of infectious diseases. Although vision-based solutions such as facial recognition can potentially scale to millions of people for identification, the privacy implications and laws to banning such a technology limit its applicability in the real world. Other techniques may require installations and maintenance of multiple units, and/or lack long-term re-identification capability. We present a dataset to fuse WiFi Channel State Information (CSI) and camera-based location information for person identification and tracking. While previous works focused on collecting WiFi CSI from stationary transmitters and receivers (laptop, desktop, or router), our WiFi CSI data are generated from a smartphone that is carried while someone is moving. In addition, we collect camera-generated real-world coordinate for each WiFi packet that can serve as ground truth location. The dataset is collected in different environments and with various numbers of persons in the scene at several days to capture real-world variations.","PeriodicalId":177625,"journal":{"name":"Proceedings of the Third Workshop on Data: Acquisition To Analysis","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115413517","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 quest for raw signals: a quality review of publicly available photoplethysmography datasets 对原始信号的探索:对公开可用的光电容积脉搏波数据集的质量审查

Proceedings of the Third Workshop on Data: Acquisition To Analysis Pub Date : 2020-11-16 DOI: 10.1145/3419016.3431485

Florian Wolling, Kristof Van Laerhoven

{"title":"The quest for raw signals: a quality review of publicly available photoplethysmography datasets","authors":"Florian Wolling, Kristof Van Laerhoven","doi":"10.1145/3419016.3431485","DOIUrl":"https://doi.org/10.1145/3419016.3431485","url":null,"abstract":"Photoplethysmography is an optical measurement principle which is present in most modern wearable devices such as fitness trackers and smartwatches. As the analysis of physiological signals requires reliable but energy-efficient algorithms, suitable datasets are essential for their development, evaluation, and benchmark. A broad variety of clinical datasets is available with recordings from medical pulse oximeters which traditionally apply transmission mode photoplethysmography at the fingertip or earlobe. However, only few publicly available datasets utilize recent reflective mode sensors which are typically worn at the wrist and whose signals show different characteristics. Moreover, the recordings are often advertised as raw, but then turn out to be preprocessed and filtered while the applied parameters are not stated. In this way, the heart rate and its variability can be extracted, but interesting secondary information from the non-stationary signal is often lost. Consequently, the test of novel signal processing approaches for wearable devices usually implies the gathering of own or the use of inappropriate data. In this paper, we present a multi-varied method to analyze the suitability and applicability of presumably raw photoplethysmography signals. We present an analytical tool which applies 7 decision metrics to characterize 10 publicly available datasets with a focus on less or ideally unfiltered, raw signals. Besides the review, we finally provide a guideline for future datasets, to suit to and to be applicable in digital signal processing, to support the development and evaluation of algorithms for resource-limited wearable devices.","PeriodicalId":177625,"journal":{"name":"Proceedings of the Third Workshop on Data: Acquisition To Analysis","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124747712","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

Multi-city street-sidewalk imagery from pedestrian mobile cameras: dataset 来自行人移动相机的多城市街道-人行道图像:数据集

Proceedings of the Third Workshop on Data: Acquisition To Analysis Pub Date : 2020-11-16 DOI: 10.1145/3419016.3431486

Shubham Jain

引用次数: 0

Lessons from large scale campus deployment 大规模校园部署的经验教训

Proceedings of the Third Workshop on Data: Acquisition To Analysis Pub Date : 2020-11-16 DOI: 10.1145/3419016.3431490

Rishiraj Adhikary, Soham Pachpande, Nipun Batra

引用次数: 0

Pollen video library for benchmarking detection, classification, tracking and novelty detection tasks: dataset 用于基准检测、分类、跟踪和新颖性检测任务的花粉视频库:数据集

Proceedings of the Third Workshop on Data: Acquisition To Analysis Pub Date : 2020-11-16 DOI: 10.1145/3419016.3431487

Nam Cao, Matthias Meyer, L. Thiele, O. Saukh

引用次数: 2

A video dataset of a wooden box assembly process: dataset 一个木箱装配过程的视频数据集:数据集

Proceedings of the Third Workshop on Data: Acquisition To Analysis Pub Date : 2020-11-16 DOI: 10.1145/3419016.3431492

Jiayun Zhang, Petr Byvshev, Yu Xiao

引用次数: 1

Toothbrushing data and analysis of its potential use in human activity recognition applications: dataset 刷牙数据及其在人类活动识别应用中的潜在用途分析:数据集

Proceedings of the Third Workshop on Data: Acquisition To Analysis Pub Date : 2020-11-16 DOI: 10.1145/3419016.3431489

Z. Hussain, D. Waterworth, Murtadha M. N. Aldeer, W. Zhang, Quan Z. Sheng

引用次数: 3

LoED: The LoRaWAN at the edge dataset: dataset LoED:边缘的LoRaWAN数据集:数据集

Proceedings of the Third Workshop on Data: Acquisition To Analysis Pub Date : 2020-09-25 DOI: 10.1145/3419016.3431491

Laksh Bhatia, Michael J. Breza, Ramona Marfievici, J. Mccann

引用次数: 19