Research on the Protection of machine-generated data under the background of data element marketization - Jie, Li (Technium Social Sciences Journal, 2024)
Introduction: The study emphasizes non-personal data protection and proposes a way tailored to China's particular requirements for machine-generated data legal protection. Integrating foreign experiences into legislation is the goal. Aim and objectives: This study examines machine-generated data legal protections throughout data marketization to understand and improve its protection in changing digital environments. Method: This study compared China's growing data protection rules to Europe's GDPR and the US's CCPA. The study uses the Penta Helix framework to evaluate non-personal data in Shenzhen, Southern China, using document reviews, field observations, interviews, and surveys. Data quality, commercial sector compliance, public sector effect, academic contributions, civil society involvement, innovation, and ethics are evaluated. Result: This study compares Chinese and American non-personal data governance. The Cybersecurity and Data Security Law in China and the California Consumer Privacy Act in the US are highlighted. Clear frameworks and international coordination are needed to define and protect non-personal data. The private sector needs, public sector governance, academia's concerns, and civil society's participation. Both countries must balance data-driven innovation with privacy, security, and ethics. Conclusion: Safeguarding China's non-personal data rights, particularly machine-generated data, is imperative, requiring global perspectives, rigorous legal analysis, and collaboration to navigate challenges.