Data Fusion Challenges Privacy: What Can Privacy Regulation Do?
G\'abor Erd\'elyi, Olivia J. Erd\'elyi, and Andreas W. Kempa-Liehr

TL;DR
This paper critiques current privacy regulations' limitations in addressing AI-driven data fusion, highlighting the need to treat anonymized data as protected and to prioritize regulation based on privacy risk levels.
Contribution
It identifies two fundamental assumptions in privacy regulation that are flawed and proposes reforms to treat anonymized data as protected and to prioritize interventions based on privacy risks.
Findings
Current regulations overlook privacy risks in anonymized data.
People often cannot assess privacy risks due to technical complexity.
Proposes regulatory reforms for better privacy protection.
Abstract
This paper focuses on some shortcomings in current privacy and data protection regulations' ability to adequately address the ramifications of AI-driven data processing practices, in particular where data sets are combined and processed by AI systems. We raise attention to two regulatory anomalies related to two fundamental assumptions underlying traditional privacy and data protection approaches: (1) Only Personally Identifiable Information (PII) and Personal Data (PD) require privacy protection: Privacy and data protection regulations are only triggered with respect to PII/PD, but not anonymous data. This is not only problematic because determining whether data falls in the former or latter category is no longer straightforward, but also because privacy risks associated with data processing may exist whether or not an individual can be identified. (2) Given sufficient information…
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Taxonomy
TopicsLegal and Policy Issues
