Data Breaches: European Union and Canada
Notice bibliographique
Résumé
A data breach occurs when an individual???s name and medical/financial records are potentially put at risk. Antiquated laws, malicious or criminal attacks, system glitches, and human error are all factors that put individuals??? personal data at risk. Organizations that fail to exercise due care when handling and processing personal data leave themselves vulnerable to costly consequences. \nIn recent years, the EU has been experiencing an increase in legal disputes related to the collection and handling of data. EU organizations located in France, Germany, Italy and the UK spent an average of $3.27m (USD) on data breaches in 2017 per a study performed by Ponemon Institute. Legal fees, compliance failures, use of mobile platforms, and cloud migration are factors that can increase an organization???s cost of a data breach. Legal services related to compliance failures account for 4%-7% of this average total cost. All of these countries, except for Germany, have had an increase in compliance related legal expenditures over the past few years. \nThe privacy law currently in effect in the European Union (EU) is Directive 95/46/EC. It was enacted in 1995, when the internet was in its infancy. It prevents the free flow of personal data across the EU countries. This directive has not been implemented consistently within the EU due to differences in its interpretation across countries. As a result, there are varying levels of personal data protection across the continent. \nFinally, in May 2018, Directive 95/46/EC will be replaced with the General Data Protection Regulation (GDPR). This new framework, informed by all the changes in the world of cyberspace, will provide uniformity of data protection across the EU and increase all individuals??? rights to control and protect their data. It will become the sole piece of legislation that addresses the appropriate handling and collection of data. This differs from the USA where regulations change based on data type and industry. GDPR represents a major overhaul. Compliance will be closely monitored by a Supervisory Authority. \nGDPR might be effective in standardizing data protection across the EU. However, this study also discusses the redress that individuals have in the event of a data breach. The private right of class action lawsuits against offending organizations varies among the countries within the EU. For example, in Finland and Hungary, only public authorities hold the right to bring a lawsuit on behalf of affected parties. In contrast, only consumer organizations can file on behalf of groups in Greece and France. The Netherlands allows for both public authorities and consumer organizations to represent affected parties. Bulgaria, Italy, Spain, UK, Germany, Portugal, and Sweden allow for public authorities and individuals to file. Compared to the USA, the EU collective redress pursuits lack strength and uniformity. \nIn order to guard against future data breaches and fulfill the obligation of protecting personal data, organizations must implement preventive measures and put into practice GDPR???s robust compliance standards. These actions would limit an organization???s expenditures and assist in customer retention. Hefty settlements could be minimized and organizations could remain more profitable.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,003 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».