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Record W6967951798 · doi:10.5281/zenodo.10047896

La certificazione ai sensi del GDPR: uno strumento di accountability per lo sviluppo della data protection

2023· article· en· W6967951798 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsData Protection Act 1998General Data Protection RegulationCertificationAccountabilityEuropean unionInformation privacyConsumer protectionInformation privacy law

Abstract

fetched live from OpenAlex

The protection of personal data is a highly topical and relevant issue, especially in light of recent developments in computational science and artificial intelligence. These sectors, in fact, offer new possibilities for the collection, analysis, and use of personal data, but also present new challenges and risks for the protection of privacy and fundamental rights of individuals. With the emergence of these new challenges, the application of the General Data Protection Regulation (GDPR) has also been influenced, leading to new solutions for managing informational privacy to adapt to new needs and ensure a balance between innovation and confidentiality. This is also reflected in numerous rulings of the Court of Justice of the European Union and regulatory interventions by the European Data Protection Board (EDPB) and national data protection authorities, all aimed at ensuring that the European technological revolution places data protection at the top of the priority list. Considering this context, an important tool has recently emerged to ensure the correct implementation of data protection measures, namely certification mechanisms. This paper aims to conduct a thorough analysis of the role of data protection certifications as an effective tool for accountability in demonstrating compliance with GDPR regulations. These tools, as provided in art. 42 and 43 of Regulation (EU) 2016/679, allow for the attestation of the adequacy and effectiveness of technical and organizational measures taken to prevent risks to the rights and freedoms of individuals arising from the processing of personal data. Considering the complexities of certification mechanisms, will be examined in depth the obligations and primary guarantees that must be implemented in accordance with art. 6, par. 2 of the GDPR to lawfully conduct personal data processing. Furthermore, we will outline the organizational methodology that an organization must adopt to document and be accountable for its processing activities. In the second chapter, will be considered the concepts and requirements necessary for the establishment, creation, approval and allocation of certification schemes, identifying their scope and applicability based on art. 42 and 43 of the GDPR. These characteristics represent the most significant challenge in the certification discipline. Indeed, the Regulation is silent on dictating the conditions under which certification criteria should be developed. Due to this uncertainty, the intervention of the EDPB has become necessary to identify the key features of certification criteria on which the mechanisms should be based. Nevertheless, some aspects of the certification process remain uncovered. Finally, the thesis will explore the legal, as well as reputational, advantages and consequences resulting from participation in a certification mechanism, both for data controllers and data subjects. From the legal and regulatory aspects outlined in the earlier chapters, will be moved on to address the practical aspect, represented by the existing certification mechanisms that have been approved under art. 42 of the GDPR. Will be scrutinized the main features of these solutions, such as their target of evaluation, functionalities, control criteria, and post-issuance verification mechanisms, in order to understand their effectiveness in establishing an appropriate technical and organizational framework for ensuring the proper processing of personal data. The paper continues by emphasizing the importance of certifications for the protection of personal data as a tool for accountability, transparency and trust in the digital market, as well as an opportunity for development and innovation for businesses operating in the digital services and artificial intelligence sectors. This examination will be conducted by observing the various points of contact between the certification mechanisms under the GDPR and the new legislative initiatives put forth by the European Commission from 2020 to date to address the new digital revolution stemming from datafication. Furthermore, the analysis has been enriched by describing the regulatory framework of some non-European jurisdictions. This comparative analysis allows to understand the role that privacy or data protection certifications play in strengthening national regulations and the culture related to the data protection. The results obtained from this study have shown how the "alignment" with the European Union's regulations has influenced the legislation of the United Kingdom in providing for co-regulation mechanisms that facilitate the implementation of obligations prescribed by the relevant national data protection regulations (UK-GDPR). In the United States and Canada, on the other hand, the situation is different: privacy certifications represent an attempt at private self-regulation that, in the absence of any public oversight, is at a higher risk of being susceptible to market abuses. Certifications under the GDPR are certainly not a cure-all for resolving all the challenges that may characterize personal data processing, especially in more complex scenarios. However, they can help lay a solid foundation for effectively designing the technical and organizational measures required to meet the accountability principle. Adherence to a certification mechanism, as well as a code of conduct, represents the best option for ensuring transparency and the security of personal data processing, potentially increasing the trust of stakeholders in digital services and new technologies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.010

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.119
GPT teacher head0.331
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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