The European Union general data protection regulation: what it is and what it means
arXiv:2510.02861 · doi:10.1080/13600834.2019.1573501
Abstract
This paper introduces the strategic approach to regulating personal data and the normative foundations of the European Union's General Data Protection Regulation ('GDPR'). We explain the genesis of the GDPR, which is best understood as an extension and refinement of existing requirements imposed by the 1995 Data Protection Directive; describe the GDPR's approach and provisions; and make predictions about the GDPR's implications. We also highlight where the GDPR takes a different approach than U.S. privacy law. The GDPR is the most consequential regulatory development in information policy in a generation. The GDPR brings personal data into a detailed regulatory regime, that will influence personal data usage worldwide. Understood properly, the GDPR encourages firms to develop information governance frameworks, to in-house data use, and to keep humans in the loop in decision making. Companies with direct relationships with consumers have strategic advantages under the GDPR, compared to third party advertising firms on the internet. To reach these objectives, the GDPR uses big sticks, structural elements that make proving violations easier, but only a few carrots. The GDPR will complicate and restrain some information-intensive business models. But the GDPR will also enable approaches previously impossible under less-protective approaches.
Cited by in corpus (14)
- GDPR Compliant Blockchains-A Systematic Literature Review
- The regulation of online political micro-targeting in Europe
- Human-GDPR Interaction: Practical Experiences of Accessing Personal Data
- Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
- Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
- FedScore: A privacy-preserving framework for federated scoring system development
- Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives
- Towards Natural Machine Unlearning
- Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods
- Understanding Worldwide Private Information Collection on Android
- Promoting Ethical Awareness in Communication Analysis: Investigating Potentials and Limits of Visual Analytics for Intelligence Applications
- MUSO: Achieving Exact Machine Unlearning in Over-Parameterized Regimes
- Approach for GDPR Compliant Detection of COVID-19 Infection Chains
- Seeing Isn't Believing: Addressing the Societal Impact of Deepfakes in Low-Tech Environments