scholarly journals Digitalization of Animal Farming

2020 ◽  
Author(s):  
Suresh Neethirajan
Keyword(s):  
Big Data ◽  
Environmental Sustainability ◽  
Data Privacy ◽  
Economic Losses ◽  
Full Potential ◽  
Complex Data ◽  
Animal Products ◽  
Animal Agriculture ◽  
Animal Farming ◽  
Blockchain Technology

As the global human population increases, animal agriculture must adapt to provide more animal products while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this review is to discuss the digitalization of animal farming with Precision Livestock Farming (PLF) technologies, specifically biosensors, big data, and block chain technology. Biosensors are noninvasive or invasive sensors that monitor an animal’s health and behavior in real time, allowing farmers to monitor individual animals and integrate this data for population-level analyses. The data from the sensors is processed using big data-processing techniques such as data modelling. These technologies use algorithms to sort through large, complex data sets to provide farmers with biologically relevant and usable data. Blockchain technology allows for traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. With these PLF technologies, animal agriculture can become more transparent and regain consumer trust. While the digitalization of animal farming has the potential to address a number of pressing concerns, these technologies are relatively new. The implementation of PLF technologies on farms will require increased collaboration between farmers, animal scientists, and engineers to ensure that technologies can be used in realistic, on-farm conditions. These technologies will call for data models that can sort through large amounts of data while accounting for specific variables and ensuring automation, accessibility, and accuracy of data. Issues with data privacy, security, and integration will need to be addressed before there can be multi-farm databases. Lastly, the usage of blockchain technology in animal agriculture is still in its infancy; blockchain technology has the potential to improve the traceability and transparency of animal products, but more research is needed to realize its full potential. The digitalization of animal farming can supply the necessary tools to provide sustainable animal products on a global scale.

scholarly journals A Comprehensive Survey on Local Differential Privacy

2020 ◽  
Vol 2020 ◽  
pp. 1-29 ◽  
Author(s):  
Xingxing Xiong ◽  
Shubo Liu ◽  
Dan Li ◽  
Zhaohui Cai ◽  
Xiaoguang Niu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Statistical Learning ◽  
Data Privacy ◽  
Statistical Estimation ◽  
Differential Privacy ◽  
Future Research ◽  
Reference Source ◽  
Complex Data ◽  
Comprehensive Survey

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

scholarly journals Big Data and the Ethical Implications of Data Privacy in Higher Education Research

2020 ◽  
Vol 12 (20) ◽  
pp. 8744
Author(s):  
Diana Florea ◽  
Silvia Florea
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Education Research ◽  
Private Information ◽  
Data Privacy ◽  
Complex Nature ◽  
Complex Data ◽  
Higher Education Research ◽  
Ethical Implications ◽  
The Impact

Despite the claimed worth and huge interest regarding the increasing volumes of complex data sets and the rewarding promise to improve research, there is, however, a growing concern regarding data privacy that affects both qualitative and quantitative higher education research. Within the contemporary debates on the impact of Big Data on the nature of higher education research and the effective ways to harmonize Big Data practice with privacy restrictions and regulations, this study sets out to qualitatively examine current issues regarding data privacy, anonymity, informed consent and confidentiality in data-centric higher education research, with a focus on the data collector, data subject and data user. We argue that within current regulations, data protection of research subjects concerns more data collection and disclosure and insufficiently describes use, having procedural implications for both the complex nature of higher education (HE) research and the type of research data being collected. We work our argument through an examination of several factors that call for a reconsideration of data privacy and access to private information in HE research. The conclusions indicate that Big Data-centric HE research is increasingly becoming a mainstream research paradigm which needs to address critical data privacy issues before being widely embraced.

scholarly journals A Framework Of Big Data As Service Platform For Access Control & Privacy Protection Using Blockchain Network

2021 ◽  
Vol 12 (11) ◽  
pp. 476-485
Author(s):  
Santosh Kumar Sharma, Et. al.
Keyword(s):  
Big Data ◽  
Access Control ◽  
Privacy Protection ◽  
Data Privacy ◽  
Cloud Platform ◽  
Transaction Data ◽  
Blockchain Technology ◽  
Event Log ◽  
Data As A Service ◽  
Entire Network

Big Data As A Service Is Used In Today’s Scenario To Handle And Process The Big Amount Of Data Which Are Generated From Different Source Every Day. Since Data Is Stored On The Cloud Platform, The System Could Suffer A Failure And Give Attackers The Opportunity To Launch Various Categories Of Attacks.Manyresearcheshave Been Done In This Domain To Provide Security And Protection To The Data On Cloud. The Blockchain Technology Is A Secure, Distributed And Privacy-Preserving Decentralized Ledger Where The Transactions Are Flexible, Secure,Verifiable And Permanent Way.Here, The Transaction Data Is Encrypted Andkept In A Wrapped Block (I.E., Record) Which Are Spreadthrough The N/W In A Provable And Unabashedmode Across The Entire Network To Enhance Information Security And Data Privacy. In This Paperwe Have Proposed A Framework For An Access Control With Privacy Protection In Bdaas Based On Blockchain  Technology. Here Blockchain Technology Is Used Only For Storing The Transaction Log Information Whenever Any Kind Of Event Log Occurred In System.

Learning Analytics Considered Harmful

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Research Community ◽  
Current Status ◽  
Online Course ◽  
Full Potential

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Experimental Result ◽  
Map Reduce ◽  
Distributed Environment ◽  
Top Down ◽  
Two Phase ◽  
Data Anonymization ◽  
Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

scholarly journals Big data for climate action or climate action for big data?

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172098203
Author(s):  
Maria I Espinoza ◽  
Melissa Aronczyk
Keyword(s):  
Big Data ◽  
Environmental Sustainability ◽  
Global Climate ◽  
Intergovernmental Organizations ◽  
Address Climate Change ◽  
Data Practices ◽  
Climate Action ◽  
User Data ◽  
Climate Crisis ◽  
Depth Interviews

Under the banner of “data for good,” companies in the technology, finance, and retail sectors supply their proprietary datasets to development agencies, NGOs, and intergovernmental organizations to help solve an array of social problems. We focus on the activities and implications of the Data for Climate Action campaign, a set of public–private collaborations that wield user data to design innovative responses to the global climate crisis. Drawing on in-depth interviews, first-hand observations at “data for good” events, intergovernmental and international organizational reports, and media publicity, we evaluate the logic driving Data for Climate Action initiatives, examining the implications of applying commercial datasets and expertise to environmental problems. Despite the increasing adoption of Data for Climate Action paradigms in government and public sector efforts to address climate change, we argue Data for Climate Action is better seen as a strategy to legitimate extractive, profit-oriented data practices by companies than a means to achieve global goals for environmental sustainability.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

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