International Law and New Challenges to Democracy in the Digital Age: Big Data, Privacy and Interferences with the Political Process

2019 ◽  
Author(s):  
Dominik Steiger
Keyword(s):  
Big Data ◽  
International Law ◽  
Data Privacy ◽  
Political Process ◽  
Digital Age ◽  
The Political ◽  
Big Data Privacy ◽  
New Challenges

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.

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

Versatile lightweight key distribution for big data privacy in vehicular ad hoc networks

2015 ◽  
Vol 28 (10) ◽  
pp. 2920-2939 ◽  
Author(s):  
Bo Qin ◽  
Linxiao Wang ◽  
Yujue Wang ◽  
Qianhong Wu ◽  
Wenchang Shi ◽  
...  
Keyword(s):  
Big Data ◽  
Ad Hoc Networks ◽  
Data Privacy ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Key Distribution ◽  
Big Data Privacy ◽  
Hoc Networks

Driving for Big Data? Privacy Concerns in Vehicular Networking

2014 ◽  
Vol 12 (1) ◽  
pp. 77-79 ◽  
Author(s):  
David Eckhoff ◽  
Christoph Sommer
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Concerns ◽  
Vehicular Networking ◽  
Big Data Privacy

scholarly journals Big Data Privacy in Healthcare Moroccan Context

2015 ◽  
Vol 63 ◽  
pp. 575-580 ◽  
Author(s):  
Bouhriz Mounia ◽  
Chaoui Habiba
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Big Data Privacy

Scalable Big Data Privacy with MapReduce

2018 ◽  
pp. 1-8
Author(s):  
Sibghat Ullah Bazai ◽  
Julian Jang-Jaccard ◽  
Xuyun Zhang
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Big Data Privacy
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