Date of Award
5-2025
Document Type
Dissertation
Degree Name
Doctor of Juridical Science (SJD)
Abstract
Businesses today collect vast amounts of personal data to generate inferences that predict consumer behavior, optimize engagement, and drive sales. While this practice is highly profitable, it raises serious privacy concerns—especially when individuals remain unaware of the inferences drawn about them or are denied meaningful control over their personal data. Although frameworks like the General Data Protection Regulation (GDPR) recognize inferences about individuals as personal data, companies frequently invoke trade secret protections to withhold access to these inferences, limiting transparency. As data analytics technologies evolve, so too do the risks of privacy loss, discrimination, and manipulation.
This Dissertation explores the intersection of trade secrecy and data protection law, focusing on the legal and practical tensions that arise in the context of inference generation. Chapter 2 lays the foundation by examining the growing significance of inference practices and the legal barriers—especially those under EU and U.S. law—that restrict consumer access to their inferences. It calls for a critical reassessment of trade secret doctrine in light of personal data rights. Chapter 3 traces the historical evolution of trade secret law, highlighting its shift from protecting fair competition to shielding data from consumers and public oversight.
Chapter 4 proposes legal and policy reforms to recalibrate trade secret protections when they conflict with privacy rights. It draws lessons from other intellectual property regimes and suggests accountability mechanisms to strengthen data controllers' obligations in responding to access requests. Chapter 5 serves as a “test suite,” evaluating the practical and legal challenges of implementing a meaningful Right of Access to Inferences. Finally, Chapter 6 synthesizes the findings and calls on policymakers to re-examine the boundaries between trade secrecy and privacy law, urging a more transparent, rights-respecting digital ecosystem.
Recommended Citation
Tantratian, Attamongkol, "Reexamining Trade Secrecy for Privacy: Transparency in AI-Generated Inferences" (2025). Maurer Theses and Dissertations. 134.
https://www.repository.law.indiana.edu/etd/134
Included in
Intellectual Property Law Commons, Privacy Law Commons, Science and Technology Law Commons