Privacy Issues and Data Protection in Big Data: A Case Study Analysis under GDPR

PurposeLooking at the limits of artificial intelligence (AI) and controlling based on complexity and system-theoretical deliberations, the authors aimed to derive a future outlook of the possible applications and provide insights into a future complementary of human–machine information processing. Derived from these examples, the authors propose a research agenda in five areas to further the field.Design/methodology/approachThis article is conceptual in its nature, yet a theoretically informed semi-systematic literature review from various disciplines together with empirically validated future research questions provides the background of the overall narration.FindingsAI is found to be severely limited in its application to controlling and is discussed from the perspectives of complexity and cybernetics. A total of three such limits, namely the Bremermann limit, the problems with a partial detectability and controllability of complex systems and the inherent biases in the complementarity of human and machine information processing, are presented as salient and representative examples. The authors then go on and carefully illustrate how a human–machine collaboration could look like depending on the specifics of the task and the environment. With this, the authors propose different angles on future research that could revolutionise the application of AI in accounting leadership.Research limitations/implicationsFuture research on the value promises of AI in controlling needs to take into account physical and computational effects and may embrace a complexity lens.Practical implicationsAI may have severe limits in its application for accounting and controlling because of the vast amount of information in complex systems.Originality/valueThe research agenda consists of five areas that are derived from the previous discussion. These areas are as follows: organisational transformation, human–machine collaboration, regulation, technological innovation and ethical considerations. For each of these areas, the research questions, potential theoretical underpinnings as well as methodological considerations are provided.

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“…(5) How to algorithmically define and enforce data rights and ensure protection and compliance to data regulations (Gruschka et al, 2018)?…”

Section: Discussing Research Agenda In Five Areasmentioning

confidence: 99%

Limits of artificial intelligence in controlling and the ways forward: a call for future accounting research

Losbichler

Lehner

2021

JAAR

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“…It also boosts organizations business and helps them in their smooth running without any hurdle of acquiring citizens data (without trust citizens usually do not share their data when required by the organizations, refer Section VI-D2 for discussion on mistrust between data provider and data handler). Even with all these obvious advantages, organizations in the past were rigid to adapt (at present they are forced to adapt) privacy regulations imposed by GDPR [142]. This is due to the fact that enterprises and organizations were facing challenges in implementing these regulations [143].…”

Section: Popular Data Protection Regulations and Their Challengementioning

confidence: 99%

Secondary Use of Electronic Health Record: Opportunities and Challenges

Shah¹,

Khan²

2020

IEEE Access

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In the present technological era, healthcare providers generate huge amounts of clinical data on a daily basis. Generated clinical data is stored digitally in the form of Electronic Health Record (EHR) as a central data repository of hospitals. Data contained in EHR is not only used for the patients' primary care but also for various secondary purposes such as clinical research, automated disease surveillance and clinical audits for quality enhancement. Using EHR data for secondary purposes without consent or in some cases even with consent creates privacy issues. Secondly, EHR data is also made accessible to various stakeholders including different government agencies at various geographical sites through wired or wireless networks. Sharing of EHR across multiple agencies makes it vulnerable to cyber attacks and also makes it difficult to implement strict privacy laws as in some cases data is shared with organization that is governed by specific regional law. Privacy of an individual could be severely affected when their sensitive private information contained in EHR is leaked or exposed to the public. Data leaks can cause financial losses or an individual may encounter social boycott if their medical condition is exposed in public. To protect patients personal data from such threats, there exists different privacy regulations such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA) and My Health Record (MHR). However, continually evolving state-of-the-art techniques in Machine Learning (ML), Data Analytics (DA) and hacking are making it even more difficult to completely protect an individual's / patient's privacy. In this article, we have systematically examined various secondary uses of EHR with the aim to highlight how these secondary uses affect patients' privacy. Secondly, we have critically analyzed GDPR & HIPAA regulations and highlighted their possible areas of improvement, considering escalating use of technology and different secondary uses of EHR. INDEX TERMS Electronic Health Records (EHR), ethical concerns, General Data Protection Regulation (GDPR), privacy, secondary uses of EHR.

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“…This poses limitations on the type of information that a company can collect and further accentuate the signal problem discussed above. The renewed attention for privacy and stricter regulations accentuate the compliance to the existing set of rules that pose information privacy law at the center of the complex network of relations in data production (Gruschka et al, 2018 ). Furthermore, GDPR poses part of the power directly in the consumer's hand who can forbid certain uses of data that he or she is willingly sharing (Yeh, 2018 ).…”

Section: A (R)evolving Paradigmmentioning

confidence: 99%

Big Data and the Little Big Bang: An Epistemological (R)evolution

Balazka

Rodighiero

2020

Front. Big Data

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Starting from an analysis of frequently employed definitions of big data, it will be argued that, to overcome the intrinsic weaknesses of big data, it is more appropriate to define the object in relational terms. The excessive emphasis on volume and technological aspects of big data, derived from their current definitions, combined with neglected epistemological issues gave birth to an objectivistic rhetoric surrounding big data as implicitly neutral, omni-comprehensive, and theory-free. This rhetoric contradicts the empirical reality that embraces big data: (1) data collection is not neutral nor objective; (2) exhaustivity is a mathematical limit; and (3) interpretation and knowledge production remain both theoretically informed and subjective. Addressing these issues, big data will be interpreted as a methodological revolution carried over by evolutionary processes in technology and epistemology. By distinguishing between forms of nominal and actual access, we claim that big data promoted a new digital divide changing stakeholders, gatekeepers, and the basic rules of knowledge discovery by radically shaping the power dynamics involved in the processes of production and analysis of data.

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Privacy Issues and Data Protection in Big Data: A Case Study Analysis under GDPR

Cited by 78 publications

References 20 publications

Limits of artificial intelligence in controlling and the ways forward: a call for future accounting research

Limits of artificial intelligence in controlling and the ways forward: a call for future accounting research

Secondary Use of Electronic Health Record: Opportunities and Challenges

Big Data and the Little Big Bang: An Epistemological (R)evolution

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