Scholarlink

<p>In today’s digital landscape, big data analytics (BDA) are becoming increasingly important. Robust strategies for safeguarding privacy and data protection are essential. This paper focuses on improving data protection impact assessments (DPIAs) in this field, ensuring alignment with the principles of the General Data Protection Regulation (GDPR). Using a Delphi approach and individual expert interviews, we validated nine privacy touch points (PTPs) for adapting DPIA methodology to BDA. First identified in our earlier research, these address issues such as consent nuances, definitions of data control, and challenges like re-identification and discrimination. The outcome is a framework tailored to the specific demands of these technologies. This study stands out for its comprehensive analysis of the PTPs and for offering actionable recommendations to strengthen DPIA frameworks. With the growing influence of artificial intelligence and large language models, the role of BDA will continue to expand. Our work therefore contributes both academically and practically by advancing DPIA practices and guiding policymakers, businesses, and privacy advocates.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Establishing a comprehensive data protection impact assessment methodology for big data analytics in compliance with the general data protection regulation

  • 期刊论文

  • 发表日期：

    2025年11月6日

查看全文
  • Georgios Georgiadis,
  • Geert Poels

摘要

In today’s digital landscape, big data analytics (BDA) are becoming increasingly important. Robust strategies for safeguarding privacy and data protection are essential. This paper focuses on improving data protection impact assessments (DPIAs) in this field, ensuring alignment with the principles of the General Data Protection Regulation (GDPR). Using a Delphi approach and individual expert interviews, we validated nine privacy touch points (PTPs) for adapting DPIA methodology to BDA. First identified in our earlier research, these address issues such as consent nuances, definitions of data control, and challenges like re-identification and discrimination. The outcome is a framework tailored to the specific demands of these technologies. This study stands out for its comprehensive analysis of the PTPs and for offering actionable recommendations to strengthen DPIA frameworks. With the growing influence of artificial intelligence and large language models, the role of BDA will continue to expand. Our work therefore contributes both academically and practically by advancing DPIA practices and guiding policymakers, businesses, and privacy advocates.