Big Data and security policies: Towards a framework for regulating the phases of analytics and use of Big Data

Broeders, Dirk; Schrijvers, Erik; Sloot, Bart van der; Brakel, Rosamunde Van; Hoog, Josta de; Ballin, E.M.H. Hirsch

doi:10.1016/j.clsr.2017.03.002

Cited by 53 publications

(35 citation statements)

References 30 publications

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“…In the previous literature, many studies described the value of perceived security for the adoption of different analytical information systems (i.e., cloud computing [67], B2C electronic commerce [68], online markets [69], and electronic health record systems [70]). The use of BDA systems has some reservations in the minds of users regarding information security in the form of informational fraud, misuse of sensitive information and use by various unconcerned departments [71,72]. Prior literature on BDA and its adoption has not emphasized security of information sufficiently [72,73].…”

Section: Perceived Securitymentioning

confidence: 99%

“…The use of BDA systems has some reservations in the minds of users regarding information security in the form of informational fraud, misuse of sensitive information and use by various unconcerned departments [71,72]. Prior literature on BDA and its adoption has not emphasized security of information sufficiently [72,73]. Based on the prior literature, one key feature that can affect BDA adoption by healthcare organizations is the security of the analytical tools used to generate useful information.…”

Section: Perceived Securitymentioning

confidence: 99%

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Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

et al. 2019

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IntroductionBig data analytics (BDA) is a course of action to examine large and complex data sets (i.e., big data) and select veiled information that can help organizations with efficient decision making [1]. The volume of data related to healthcare organizations has grown dramatically in past years and is expected to increase in coming years due to the use of innovative technologies [2]. Meanwhile, healthcare reimbursement methods are changing, and pay for performance is an emerging factor in the current healthcare environment. Recently, healthcare organizations have only focused on profit and have neglected to acquire the essential tools, infrastructure, and technologies for effective control of big data to ensure citizens' health care [3,4]. Big data incorporates features such as variety, velocity, and veracity. BDA techniques can be applied to the massive amount of prevailing patient-related medical information to analyze outcomes for improvement of the healthcare sector [5,6]. Using BDA in the healthcare sector will help inform each physician of the medical histories of individuals and the population and enable appropriate Abstract Big data analytics is gaining substantial attention due to its innovative contribution to decision making and strategic development across the healthcare field. Therefore, this study explored the adoption mechanism of big data analytics in healthcare organizations to inspect elements correlated to behavioral intention using the technology acceptance model and task-technology fit paradigm. Using a survey questionnaire, we analyzed 224 valid responses in AMOS v21 to test the hypotheses. Our results posit that the credentials of the technology acceptance model together with task-technology fit contribute substantially to the enhancement of behavioral intentions to use the big data analytics system in healthcare, ultimately leading towards actual use. Meanwhile, trust in and security of the information system also positively influenced the behavioral intention for use. Employee resistance to change is a key factor underlying failure of the innovative system in organizations and has been proven in this study to negatively moderate the relationship between intention to use and actual use of big data analytics in healthcare. Our results can be implemented by healthcare organizations to develop an understanding of the implementation of big data analytics and to promote psychological empowerment of employees to accept this innovative system. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Section: Perceived Securitymentioning

confidence: 99%

Section: Perceived Securitymentioning

confidence: 99%

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

et al. 2019

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“…Los datos son esenciales para el desarrollo de sus actividades cotidianas, así como para ayudar a la dirección de las empresas a alcanzar sus objetivos y a tomar las mejores decisiones a partir de la información que se extrae de ellas [2]. Los desarrollos tecnológicos y nuevas aplicaciones continúan alimentando el debate sobre lo que define a Big Data y lo distingue de las anteriores formas de análisis de datos [3]. No existe un consenso real en cuanto a sus características clave, aunque la mayoría de las definiciones de Big Data se refieren a las tres Vs [4].…”

Section: Palabras Clave-big Data Análisis Y Gestión De Riesgos Seguunclassified

MARISMA-BiDa: Gestión y Control del riesgo en Big Data. Caso de Estudio

Rosado¹,

Moreno²,

Sánchez³

et al. 2020

Seguridad Informática. X Congreso Iberoamericano, CIBSI 2020

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“…First, there is now great public awareness, thanks to the Snowden revelations and the Facebook/Cambridge Analytica scandal, about the realities and impact of state and corporate data harvesting and profiling. 62 For example, a 2018 survey by The Atlantic magazine found that 99% of respondents were aware of the Cambridge Analytica story and among those, 42% indicated they had changed their online behaviour in response to the scandal. 63 A 2016 Pew Internet survey, meanwhile, found that 49% of respondents said their personal data was "less secure" compared to five years prior.…”

Section: Post-snowden Impactmentioning

confidence: 99%

Chilling effects and transatlantic privacy

Penney¹

2019

European Law Journal

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Can the European and American privacy divide be bridged? Bilyana Petkova, in this issue, offers compelling reasons to be sceptical. One recent solution, advanced by Pierluigi Perri and David Thaw, is that common concerns about chilling effects can bridge that divide. However, their discussion of chilling effects was narrow and their analysis limited to procedural transatlantic convergence. This essay explores this idea with a more systematic and sustained discussion of chilling effects theory and research, while arguing that chilling effects does, in fact, provide possibilities for substantive transatlantic privacy. I argue that “chilling effects” is often treated as an ahistorical singular idea but there are, in fact, three separate paradigms of chilling effects theory, research and understanding: (1) speech; (2) privacy and autonomy; and (3) collectivist. I set out each and argue that the conceptualisation of chilling effects exemplified by the second paradigm—focused on privacy‐related chilling effects—offers a shared normative and theoretical foundation to bridge the transatlantic privacy divide. I also explore how new chilling effects theory and research can impact substantive and procedural transatlantic privacy efforts, including re‐thinking consent; empowering stronger judicial enforcement of privacy claims; and balancing competing claims in substantive proposals like the Right to be Forgotten (RTBF).

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Big Data and security policies: Towards a framework for regulating the phases of analytics and use of Big Data

Cited by 53 publications

References 30 publications

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

MARISMA-BiDa: Gestión y Control del riesgo en Big Data. Caso de Estudio

Chilling effects and transatlantic privacy

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