The “Right to Be Forgotten” in the GDPR: Implementation Challenges and Potential Solutions

Πολίτου, Ευγενία; Alepis, Efthimios; Virvou, Maria; Patsakis, Constantinos

doi:10.1007/978-3-030-85443-0_4

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…So there are limits to how much extrapolation can be employed given the differing contexts. The third type of legal literature focuses on the barriers and challenges to applying the RTBF, and either criticises the vagueness of the legal requirements for implementing the right, or expresses the technical difficulty and impracticality of applying the right [16,36,37]. These challenges inspired the authors to construct a decision-making flow that could aid in overcoming these challenges.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, restriction can be imposed as a result of balancing different rights or interests, as stated in the CJEU judgement in the Camera di Commercio case, to balance the data subject's right to erasure and legal obligations [45]. Similar to restriction, in backup systems, it can be impractical to erase data because of the technical difficulty or security requirement [36]. In those cases, the UK DPA, the Information Commissioner (ICO), for example, directs controllers to explain this clearly to the data subject and not to use the backup data for any other purposes; to put the requested data 'beyond use', publicly and privately [26].…”

Section: Machine Learning Techniques For Erasurementioning

confidence: 99%

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A Decision-Making Process to Implement the ‘Right to Be Forgotten’ in Machine Learning

Hawkins,

Alhuwaish,

Belguith

et al. 2024

Lecture Notes in Computer Science

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The unprecedented scale at which personal data is used to train machine learning (ML) models is a motivation to examine the ways in which it can be erased when implementing the GDPR's 'right to be forgotten'. The existing literature investigating this right focus on a purely technical or legal approach, lacking the collaboration required for this interdisciplinary space. Recent works has identified there is no one solution to erasure in ML and this must therefore be decided on a case-by-case basis. However, there is an absence of guidance for controllers to follow when personal data must be erased in ML. In this paper we develop a novel, decision-making flow that encompasses the necessary considerations for a controller. Addressing, in particular, the interdisciplinary considerations relevant to the EU GDPR and data protection scholarship, as well as concepts from computer science and its application in industry. This results in several optimal solutions for the controller and data subject, differing with levels of erasure. To validate the proposed decision-making flow a real case study is discussed throughout the paper. The paper highlights the need for a clearer framework when personal data must be erased in ML; empowering the regulator, controller and data subject.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Machine Learning Techniques For Erasurementioning

confidence: 99%

A Decision-Making Process to Implement the ‘Right to Be Forgotten’ in Machine Learning

Hawkins,

Alhuwaish,

Belguith

et al. 2024

Lecture Notes in Computer Science

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“…• Ensure that data and insights belong to the stakeholder organizations who generate them and that data policies are fair and equal. Empower patients and citizens with their data, including consideration of policies such as "the right to be forgotten" (eg, Europe's General Data Protection Regulation 18 ).…”

Section: Consensus Pointsmentioning

confidence: 99%

Successfully Implementing Digital Health to Ensure Future Global Health Security During Pandemics

et al. 2022

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IMPORTANCE COVID-19 has highlighted widespread chronic underinvestment in digital health that hampered public health responses to the pandemic. Recognizing this, the Riyadh Declaration on Digital Health, formulated by an international interdisciplinary team of medical, academic, and industry experts at the Riyadh Global Digital Health Summit in August 2020, provided a set of digital health recommendations for the global health community to address the challenges of current and future pandemics. However, guidance is needed on how to implement these recommendations in practice. OBJECTIVETo develop guidance for stakeholders on how best to deploy digital health and data and support public health in an integrated manner to overcome the COVID-19 pandemic and future pandemics. EVIDENCE REVIEW Themes were determined by first reviewing the literature and Riyadh GlobalDigital Health Summit conference proceedings, with experts independently contributing ideas. Then, 2 rounds of review were conducted until all experts agreed on the themes and main issues arising using a nominal group technique to reach consensus. Prioritization was based on how useful the consensus recommendation might be to a policy maker.FINDINGS A diverse stakeholder group of 13 leaders in the fields of public health, digital health, and health care were engaged to reach a consensus on how to implement digital health recommendations to address the challenges of current and future pandemics. Participants reached a consensus on high-priority issues identified within 5 themes: team, transparency and trust, technology, techquity (the strategic development and deployment of technology in health care and health to achieve health equity), and transformation. Each theme contains concrete points of consensus to guide the local, national, and international adoption of digital health to address challenges of current and future pandemics. CONCLUSIONS AND RELEVANCEThe consensus points described for these themes provide a roadmap for the implementation of digital health policy by all stakeholders, including governments. Implementation of these recommendations could have a significant impact by reducing fatalities and uniting countries on current and future battles against pandemics.

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“…Yet, the exercise of this right has become a thorny issue in applications. Politou et al [62] discussed that the technical challenges of aligning modern systems and processes with the GDPR provisions are numerous and in most cases insurmountable. In [63] they specifically examined the implications of erasure requests on current backup systems and highlight a number of challenges pertained to the widely known backup standards, data retention policies, backup mediums, search services, and ERP systems [1].…”

Section: Related Workmentioning

confidence: 99%

Burn After Reading: Online Adaptation for Cross-domain Streaming Data

Yang¹,

Gao²,

Chen³

et al. 2021

Preprint

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In the context of online privacy, many methods propose complex privacy and security preserving measures to protect sensitive data. In this paper we argue that: not storing any sensitive data is the best form of security. Thus we propose an online framework that "burns after reading", i.e. each online sample is immediately deleted after it is processed. Meanwhile, we tackle the inevitable distribution shift between the labeled public data and unlabeled private data as a problem of unsupervised domain adaptation. Specifically, we propose a novel algorithm that aims at the most fundamental challenge of the online adaptation setting-the lack of diverse source-target data pairs. Therefore, we design a Cross-Domain Bootstrapping approach, called CRODOBO, to increase the combined diversity across domains. Further, to fully exploit the valuable discrepancies among the diverse combinations, we employ the training strategy of multiple learners with cosupervision. CRODOBO achieves state-of-the-art online performance on four domain adaptation benchmarks.

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The “Right to Be Forgotten” in the GDPR: Implementation Challenges and Potential Solutions

Cited by 6 publications

References 34 publications

A Decision-Making Process to Implement the ‘Right to Be Forgotten’ in Machine Learning

A Decision-Making Process to Implement the ‘Right to Be Forgotten’ in Machine Learning

Successfully Implementing Digital Health to Ensure Future Global Health Security During Pandemics

Burn After Reading: Online Adaptation for Cross-domain Streaming Data

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