A Theory of Privacy as Rules

2021 ◽  
pp. 35-69
Author(s):  
Neil Richards
Keyword(s):  
Big Data ◽  
Pregnant Women ◽  
Human Behavior ◽  
Data Science ◽  
Behavioral Science ◽  
Human Values ◽  
Human Beings ◽  
Real Lesson

Target Corporation’s famous use of big data to predict which of its customers were pregnant involved a potent cocktail of behavioral science and data science to influence customers’ behavior without their knowledge. In Target’s case, it sent coupons to pregnant women so as to habituate them into becoming long-term Target customers. Its real lesson is that human information confers the power to control human behavior. Rather than thinking principally about defining privacy, we should think about regulating to protect people from the power that human information confers. This conclusion has four important implications. First, it reveals that privacy is fundamentally about power—power over human beings in society. Second, struggles over “privacy” are really struggles over the rules that constrain the power that human information confers. Third, privacy rules of some sort are inevitable. Fourth, privacy should be thought of in instrumental terms to promote human values.

scholarly journals Human Behavior Analysis Using Intelligent Big Data Analytics

2021 ◽  
Vol 12 ◽  
Author(s):  
Muhammad Usman Tariq ◽  
Muhammad Babar ◽  
Marc Poulin ◽  
Akmal Saeed Khattak ◽  
Mohammad Dahman Alshehri ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Human Behavior ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Apache Spark ◽  
Social Media Data ◽  
The Social ◽  
Media Data

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted.

scholarly journals Analisis Terhadap Arah Dan Tujuan Pendidikan Nasional Pada Rencana Kerja Pemerintah Tahun 2005 – 2025

alashriyyah ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 16
Author(s):  
Mohamad Samsudin
Keyword(s):  
Strong Influence ◽  
National Development ◽  
Social Institution ◽  
Human Values ◽  
Human Beings ◽  
National Education ◽  
Nation States ◽  
Political Economic ◽  
Cultural Defense

Indonesian development runs on the foundation of Indonesia's long-term vision, namely the realization of nation-states, modern Indonesia that is safe and peaceful, fair and democratic, and prosperous by upholding human values, independence and unity based on Pancasila and The 1945 Constitution. To realize this, education as a subsystem is one of the important aspects to be considered in its direction and purpose so that education is not merely an aspect of supporting Indonesia's development, but as a locomotive of development itself. Because in reality, education is one aspect of life that is run by being influenced by various external aspects that are interrelated with each other such as political, economic, socio-cultural, defense-security aspects, even ideology has a very strong influence on the continuity of education, and vice versa. This paper aims to find out how the Long Term Development Plan (RPJP) is specifically regarding national education between 2005-2010 and 2010-2025. To achieve this goal, the author uses content analysis research using written documents that have been used as guidelines to determine the direction of the Indonesian government's policy in realizing national development. The results of the research in this paper show that the development of national education in the future is based on the paradigm of developing Indonesian people as a whole. The humanitarian dimension includes the three most basic things, namely: cognitive, affective, and psychomotor. This is based on the desire to realize the education system as a strong and authoritative social institution to empower all citizens of Indonesia to develop into quality human beings so that they are able and proactively respond to the challenges of an ever-changing era.

scholarly journals Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 136
Author(s):  
Huifen Zhou ◽  
Huiying Ren ◽  
Patrick Royer ◽  
Hongfei Hou ◽  
Xiao-Ying Yu
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Meteorological Data ◽  
Extreme Weather ◽  
Department Of Energy ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Classical Statistics

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

Towards Computational and Behavioral Social Science

2016 ◽  
Vol 21 (2) ◽  
pp. 131-140 ◽  
Author(s):  
Rosaria Conte ◽  
Francesca Giardini
Keyword(s):  
Big Data ◽  
Social Science ◽  
Data Science ◽  
Behavioral Science ◽  
Big Data Analytics ◽  
Computational Social Science ◽  
Evolution Of Cooperation ◽  
Social Phenomena ◽  
Integrative View ◽  
Social Data Mining

Abstract. In the last few years, the study of social phenomena has hosted a renewal of interest in Computational Social Science (CSS). While this field is not new – Axelrod’s first computational work on the evolution of cooperation goes back to 1981 – CSS has recently resurged under the pressure of quantitative social science and the application of Big Data analytics to social datasets. However, Big Data is no panacea and the data deluge that it provides raises more questions than it answers. The aim of this paper is to present an overview in which CSS will be introduced and the costs of CSS will be balanced against its benefits, in an attempt to propose an integrative view of the new and the old practice of CSS. In particular, two routes to integration will be drawn. First, it will be advocated that social data mining and computational modeling need to be integrated. Second, we will introduce the generative approach, aimed to understand how social phenomena can be generated starting from the micro-components, including psychological mechanisms, and we will discuss the necessity of combining it with the anticipatory, data-driven objective. By these means, Computational Social Science will develop into a more comprehensive field of Computational Social and Behavioral Science in which data science, ICT, as well as the behavioral and social sciences will be fruitfully integrated.

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

2020 ◽  
Author(s):  
Bankole Olatosi ◽  
Jiajia Zhang ◽  
Sharon Weissman ◽  
Zhenlong Li ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Big Data ◽  
South Carolina ◽  
Data Science ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Data Sources ◽  
Patient Registries ◽  
Multiple Partner ◽  
Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

Produção internacional sobre ciência orientada a dados: análise dos termos data science e e-science na scopus e na web of science

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Leilah Santiago Bufrem ◽  
Fábio Mascarenhas Silva ◽  
Natanael Vitor Sobral ◽  
Anna Elizabeth Galvão Coutinho Correia
Keyword(s):  
Big Data ◽  
Open Access ◽  
Grid Computing ◽  
Digital Library ◽  
Data Science ◽  
Web Of Science ◽  
Computer Systems ◽  
Distributed Computer Systems

Introdução: A atual configuração da dinâmica relativa à produção e àcomunicação científicas revela o protagonismo da Ciência Orientada a Dados,em concepção abrangente, representada principalmente por termos como “e-Science” e “Data Science”. Objetivos: Apresentar a produção científica mundial relativa à Ciência Orientada a Dados a partir dos termos “e-Science” e “Data Science” na Scopus e na Web of Science, entre 2006 e 2016. Metodologia: A pesquisa está estruturada em cinco etapas: a) busca de informações nas bases Scopus e Web of Science; b) obtenção dos registros; bibliométricos; c) complementação das palavras-chave; d) correção e cruzamento dos dados; e) representação analítica dos dados. Resultados: Os termos de maior destaque na produção científica analisada foram Distributed computer systems (2006), Grid computing (2007 a 2013) e Big data (2014 a 2016). Na área de Biblioteconomia e Ciência de Informação, a ênfase é dada aos temas: Digital library e Open access, evidenciando a centralidade do campo nas discussões sobre dispositivos para dar acesso à informação científica em meio digital. Conclusões: Sob um olhar diacrônico, constata-se uma visível mudança de foco das temáticas voltadas às operações de compartilhamento de dados para a perspectiva analítica de busca de padrões em grandes volumes de dados.Palavras-chave: Data Science. E-Science. Ciência orientada a dados. Produção científica.Link:http://www.uel.br/revistas/uel/index.php/informacao/article/view/26543/20114

Impact of Big Data over Telecom Industry

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Muhammad Waqar Khan ◽  
Muhammad Asghar Khan ◽  
Muhammad Alam ◽  
Wajahat Ali
Keyword(s):  
Big Data ◽  
Data Science ◽  
Cell Phones ◽  
Smart Phones ◽  
World Population ◽  
Huge Amount ◽  
Scale Down ◽  
Telecom Industry ◽  
Telecom Sector ◽  
Theoretical Computing

<p>During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currently<br />more than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. There<br />is a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones to<br />generate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected data<br />we use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data.</p>

scholarly journals Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2281
Author(s):  
Fatemeh Sarhaddi ◽  
Iman Azimi ◽  
Sina Labbaf ◽  
Hannakaisa Niela-Vilén ◽  
Nikil Dutt ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Maternal Health ◽  
Pregnant Women ◽  
Health Monitoring ◽  
Quality Care ◽  
Well Being ◽  
Pregnancy And Postpartum ◽  
Comprehensive Framework ◽  
Specific Health

Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother’s and her unborn baby’s health and well-being. Different studies have thus far proposed maternal health monitoring systems. However, they are designed for a specific health problem or are limited to questionnaires and short-term data collection methods. Moreover, the requirements and challenges have not been evaluated in long-term studies. Maternal health necessitates a comprehensive framework enabling continuous monitoring of pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system to provide ubiquitous maternal health monitoring during pregnancy and postpartum. The system consists of various data collectors to track the mother’s condition, including stress, sleep, and physical activity. We carried out the full system implementation and conducted a real human subject study on pregnant women in Southwestern Finland. We then evaluated the system’s feasibility, energy efficiency, and data reliability. Our results show that the implemented system is feasible in terms of system usage during nine months. We also indicate the smartwatch, used in our study, has acceptable energy efficiency in long-term monitoring and is able to collect reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system.

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