The politics of privacy: Planning for personal data systems as powerful technologies

1982 ◽  
Vol 70 (3) ◽  
pp. 315-316
Author(s):  
S. Birnbaum
Keyword(s):  
Author(s):  
José Moura ◽  
Carlos Serrão

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.


1983 ◽  
Vol 12 (1) ◽  
pp. 88
Author(s):  
Richard W. Wilsnack ◽  
James Rule ◽  
Douglas McAdam ◽  
Linda Stearns ◽  
David Uglow
Keyword(s):  

2018 ◽  
Vol 31 (31) ◽  
pp. 169-186
Author(s):  
Marek Mazur

The EU GDPR Regulation introduced rules and regulations on the protection of individuals with regard to the processing of their personal data regardless of their citizenship or place of residence. The article focuses on issues related directly to the regulation on the protection of personal data and related to documents that regulate the protection of personal data and their processing in public institutions in Poland. The author presents basic estimates about the entry of the GDPR Regulation, indicates the importance of individual Dobies/organisations and entities playing a key role in the protection of personal data on the territory of Poland. It describes the documents that establish minimum standards for personal data protection systems to be developed in public institutions to guarantee security. In this article, the author attempted to indicate the scope and nature of changes in personal data systems in the light of the provisions of the GDPR Regulation.


Data & Policy ◽  
2020 ◽  
Vol 2 ◽  
Author(s):  
Ina Sander

Abstract Datafied societies need informed public debate about the implications of data science technologies. At present, internet users are often unaware of the potential consequences of disclosing personal data online and few citizens have the knowledge to participate in such debates. This paper argues that critical big data literacy efforts are one way to address this lack of knowledge. It draws on findings from a small qualitative investigation and discusses the effectiveness of online critical big data literacy tools. Through pre and post use testing, the short- and longer-term influence of these tools on people’s privacy attitudes and behavior was investigated. The study’s findings suggested that the tools tested had a predominantly positive initial effect, leading to improved critical big data literacy among most participants, which resulted in more privacy-sensitive attitudes and internet usage. When analyzing the tools’ longer-term influence, results were more mixed, with evidence suggesting for some that literacy effects of the tools were short-lived, while for others they led to more persistent and growing literacy. The findings confirm previous research noting the complexity of privacy attitudes and also find that resignation toward privacy is multi-faceted. Overall, this study reaffirms the importance of critical big data literacy and produces new findings about the value of interactive data literacy tools. These tools have been under-researched to date. This research shows that these tools could provide a relevant means to work toward empowering internet users, promoting a critical internet usage and, ideally, enabling more citizens to engage in public debates about changing data systems.


Web Services ◽  
2019 ◽  
pp. 2197-2229
Author(s):  
José Moura ◽  
Carlos Serrão

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.


Author(s):  
José Moura ◽  
Carlos Serrão

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.


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