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Enregistrement 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 sur OpenAlexaboutno aff

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiquePrivacy, Security, and Data Protection
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésData Protection Act 1998General Data Protection RegulationCertificationAccountabilityEuropean unionInformation privacyConsumer protectionInformation privacy law

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,526
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0050,000
Communication savante0,0020,001
Science ouverte0,0020,003
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0050,010

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,119
Tête enseignante GPT0,331
Écart entre enseignants0,212 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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