Guidelines for GDPR compliance in Big Data systems

Rhahla, Mouna; Allegue, Sahar; Abdellatif, Takoua

doi:10.1016/j.jisa.2021.102896

Cited by 21 publications

(10 citation statements)

References 12 publications

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“…The purpose of the GDPR is that: "Every organization that deals with personal data has to comply with the GDPR to protect these rights and to be accountable while improving business models" [60]. The GPDR framework thus allows companies to comply with the following data protection principles: "Lawfulness, fairness and transparency; Purpose limitation; Accuracy; Data minimization; Integrity and confidentiality; Accountability" [60]. Hence, this framework is well adapted for Big Data management and is not involved in the other aspects of systems intelligence.…”

Section: General Frameworkmentioning

confidence: 99%

Industrial Performance: An Evolution Incorporating Ethics in the Context of Industry 4.0

et al. 2021

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This article addresses the issue of the industrial performance model and its evolution to cope with the context of Industry 4.0. With its digitalisation, intelligent/autonomous systems and wealth of data, Industry 4.0 offers opportunities that can achieve objectives better. It also presents risks and uncertainties that question the autonomy of the systems, their interaction with humans and the use of available data. The hypothesis put forward in this work is that the efficiency–effectiveness–relevance performance triangle can no longer guarantee long-term performance under these conditions and needs to be associated with an ethical dimension that allows for the risks and uncertainties relating to Industry 4.0 to be considered. Ethics is therefore considered to extend the triangle to a tetrahedron. A brief analysis of current performance management will first show the limits of the current practice in the context of Industry 4.0. The frameworks that could overcome these limits in light of new needs are then recalled and discussed, leading to the choice of ethics, whose main definitions and use in the engineering field are also introduced. The proposed (efficiency–effectiveness–relevance–ethics tetrahedron-based methodology is illustrated through a case study related to an aeronautical supplier, regarding the consequences of the implementation of a MES (Manufacturing Execution System) in terms of product traceability and operator autonomy. The discussion and prospects finally conclude this study.

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Section: General Frameworkmentioning

confidence: 99%

Industrial Performance: An Evolution Incorporating Ethics in the Context of Industry 4.0

et al. 2021

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“…The underlying theme of the proposal is the bringing of innovation into healthcare systems, and discussing its content is inevitably anchored around that theme. However, another of the principal areas for discussion is the scope for data-sharing in the context of the earlier GDPR [ 40 , 44 , 51 ]. In addition, it is also impossible to explore the implications of the proposed framework without taking full account of the issues it raises of interoperability, among distinct physical systems, and also in relation to operational aspects of healthcare such as products—and particularly medical devices and IVDR—and processes such as clinical trials and authorisation of medicines.…”

Section: Discussionmentioning

confidence: 99%

“…The GDPR has been a good lesson of what works and does not work in terms of thinking ahead on decision-making [ 41 , 45 , 51 ]. The European Parliament discussions reflected democracy in action in that it led to over 4000 amendments to the proposal—but the outcome has left considerable weaknesses in the EU, particularly as so many national divergences in its application still exist—with negative consequences for many activities, including research [ 61 ].…”

Section: Discussionmentioning

confidence: 99%

European Health Data Space—An Opportunity Now to Grasp the Future of Data-Driven Healthcare

et al. 2022

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The May 2022 proposal from the European commission for a ‘European health data space’ envisages advantages for health from exploiting the growing mass of health data in Europe. However, key stakeholders have identified aspects that demand clarification to ensure success. Data will need to be freed from traditional silos to flow more easily and to cross artificial borders. Wide engagement will be necessary among healthcare professionals, researchers, and the patients and citizens that stand to gain the most but whose trust must be won if they are to allow use or transfer of their data. This paper aims to alert the wider scientific community to the impact the ongoing discussions among lawmakers will have. Based on the literature and the consensus findings of an expert multistakeholder panel organised by the European Alliance for Personalised Medicine (EAPM) in June 2022, it highlights the key issues at the intersection of science and policy, and the potential implications for health research for years, perhaps decades, to come.

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“…For example, mathematics gave new meaning to data correlation between a low credit rating and having more car accidents cannot reveal why this happens. 53 Furthermore, data can also be seen as its original description, in particular, data from words, locations, and interactions, through the process of datafication and digitalisation. Datafication means putting a phenomenon or something in a quantified format that allows it to be tabulated and analysed, whereas digitalisation is the process of converting analogue information into the zeros and ones of binary code so computers can handle it.…”

Section: Big Data Analytics and Its Impact On Data Protection Lawmentioning

confidence: 99%

The Regulation of Personal and Non-Personal Data in the Context of Big Data

Supriyadi

2023

JHCLS

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Data protection laws provide minimum protections for personal data, as well as facilitate the free flow of such data, by setting out principles and rules for legitimate data processing. In the big data context, personal data may not be as easy to distinguish as in traditional data processing, and that makes policy-makers and businesses turn to the identifiability concept: in other words, what data are personal. This research is based on doctrinal legal researchon the legal theory (concepts, rules, and principles) concerning data protection in the EU and Indonesia. The results of the research show that the understand such paramount terminology in data protection law, relevant factors are presented to assess the direct or indirect identification of a natural person. In the EU data protection law, the test entails, for example, risk-based measures and technological development, whereas Indonesian law on data protection has not yet established such assessments. Data within big data operations traditionally falls under the scope of data protection laws only if it discloses the private life of individuals, such as names or other civil identities, but without further conditions to ascertain whether the data can be indirectly identified with an individual.

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Guidelines for GDPR compliance in Big Data systems

Cited by 21 publications

References 12 publications

Industrial Performance: An Evolution Incorporating Ethics in the Context of Industry 4.0

Industrial Performance: An Evolution Incorporating Ethics in the Context of Industry 4.0

European Health Data Space—An Opportunity Now to Grasp the Future of Data-Driven Healthcare

The Regulation of Personal and Non-Personal Data in the Context of Big Data

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