Big Data Privacy Context: Literature Effects On Secure Informational Assets

This article's objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.Comment: 21 pages, 9 figure

“You Don’t Know Where It Will Stop” -- An Inquiry into Smartphone Users' Privacy Mental Models of Contextual Integrity

The Contextual Integrity (CI) theory provides a benchmark for privacy protection or violation according to the appropriateness of information collection and flows in a certain context. As privacy threats and protections develop and vie in various mobile contexts, how smartphone users represent the benchmark CI in their minds deserves exploration. In this study, we inquired into 18 smartphone users’ privacy mental models of CI. We found that they verbalized and visualized three patterns of information flow (i.e., unidirectional lines, branching tree, and complex network) and two categories of information collection (i.e., monetization-oriented and monitoring-based). With these mental models, our participants expressed numerous privacy concerns, such as unstoppable information sharing, data monetization, and surveillance. We discussed these findings and concluded that even though mobile operating systems and apps have claimed to be privacy-friendly and protective, some users remain dubious about such claims even though their privacy mental models may not accurately reflect reality

Preparing IT Professionals of the Future! ! !

Abstract. The underlying aim that should be instilled in future I

Social injustice in surveillance capitalism

This is the final version of the article. Available from the publisher via the link in this record.A rapidly accelerating phase of capitalism based on asymmetrical personal data accumulation poses significant concerns for democratic societies, yet the concepts used to understand and challenge practices of dataveillance are insufficient or poorly elaborated. Against a backdrop of growing corporate power enabled by legal lethargy and the secrecy of the personal data industry, this paper makes explicit how the practices inherent to what Shoshana Zuboff calls ‘surveillance capitalism’ are threats to social justice, based on the normative principle that they prevent parity of participation in social life. This paper draws on Nancy Fraser’s theory of ‘abnormal justice’ to characterize the separation of people from their personal data and its accumulation by corporations as an economic injustice of maldistribution. This initial injustice is also the key mechanism by which further opaque but significant forms of injustice are enabled in surveillance capitalism—sociocultural misrecognition which occurs when personal data are algorithmically processed and subject to categorization, and political misrepresentation which renders people democratically voiceless, unable to challenge misuses of their data. In situating corporate dataveillance practices as a threat to social justice, this paper calls for more explicit conceptual development of the social harms of asymmetrical personal data accumulation and analytics, and more hopefully, attention to the requirements needed to recast personal data as an agent of equality rather than oppression

Online privatliv – udfordringer og mulige løsninger

This article discusses current challenges to privacy in the digital domain. It argues that in order to counter these challenges several interrelated responses are needed. First, the regulatory regime for data protection must be revised and strengthened. Second, privacy and data protection must be closely integrated into technical solutions and organizational procedures. ird, the concept of privacy must be revisited to more adequately re ect the dynamics of online life. is requires a more nuanced understanding of online practices and the internet’s characteristics as radical heterogeneous, socially complex and context-collapsing. Moreover, e ective protection of privacy rights requires that we explicitly formulate and enforce public policy norms for the internet’s core services, irrespective of whether these services are carried out by public or private actors.

Re-engineering justice? Robot judges, computerized courts and semi-automated legal decision-making

This paper takes a sceptical look at the possibility of advanced computer technology replacing judges. Looking first at the example of alternative dispute resolution, where considerable progress has been made in developing tools to assist parties to come to agreement, attention then shifts to evaluating a number of other algorithmic instruments in a criminal justice context. The possibility of human judges being fully replaced within the courtroom strictu sensu is examined, and the various elements of the judicial role that need to be reproduced are considered. Drawing upon understandings of the legal process as an essentially socially determined activity, the paper sounds a note of caution about the capacity of algorithmic approaches to ever fully penetrate this socio-legal milieu and reproduce the activity of judging, properly understood. Finally, the possibilities and dangers of semi-automated justice are reviewed. The risks of seeing this approach as avoiding the recognised problems of fully automated decision-making are highlighted, and attention is directed towards the problems that remain when an algorithmic frame of reference is admitted into the human process of judging

Big data privacy context: literature effects on secure informational assets

This article’s objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.Indisponível

The ghost in the legal machine : algorithmic governmentality, economy, and the practice of law

Purpose: This paper aims to investigate algorithmic governmentality – as proposed by Antoinette Rouvroy – specifically in relation to law. It seeks to show how algorithmic profiling can be particularly attractive for those in legal practice, given restraints on time and resources. It deviates from Rouvroy in two ways. First, it argues that algorithmic governmentality does not contrast with neoliberal modes of government in that it allows indirect rule through economic calculations. Second, it argues that critique of such systems is possible, especially if the creative nature of law can be harnessed effectively. Design/methodology/approach: This is a conceptual paper, with a theory-based approach, that is intended to explore relevant issues related to algorithmic governmentality as a basis for future empirical research. It builds on governmentality and socio-legal studies, as well as research on algorithmic practices and some documentary analysis of reports and public-facing marketing of relevant technologies. Findings: This paper provides insights on how algorithmic knowledge is collected, constructed and applied in different situations. It provides examples of how algorithms are currently used and how trends are developing. It demonstrates how such uses can be informed by socio-political and economic rationalities. Research limitations/implications: Further empirical research is required to test the theoretical findings. Originality/value : This paper takes up Rouvroy’s question of whether we are at the end(s) of critique and seeks to identify where such critique can be made possible. It also highlights the importance of acknowledging the role of political rationalities in informing the activity of algorithmic assemblages