I’m delighted to share the final published version of my article, Data Is What Data Does: Regulating Based on Harm and Risk Instead of Sensitive Data, 118 Nw. U. L. Rev. 1081 (2024).
This article was selected for the Future of Privacy Forum’s Privacy Papers for Policymakers Award. The Award aims to “recognize leading U.S. and international privacy scholarship that is relevant to policymakers in the U.S. Congress, federal agencies, and international data protection authorities.”
You can download my article for free here:
Here’s the abstract:
Heightened protection for sensitive data is becoming quite trendy in privacy laws around the world. Originating in European Union (EU) data protection law and included in the EU’s General Data Protection Regulation, sensitive data singles out certain categories of personal data for extra protection. Commonly recognized special categories of sensitive data include racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, health, sexual orientation and sex life, and biometric and genetic data.
Although heightened protection for sensitive data appropriately recognizes that not all situations involving personal data should be protected uniformly, the sensitive data approach is a dead end. The sensitive data categories are arbitrary and lack any coherent theory for identifying them. The borderlines of many categories are so blurry that they are useless. Moreover, it is easy to use nonsensitive data as a proxy for certain types of sensitive data.
Personal data is akin to a grand tapestry, with different types of data interwoven to a degree that makes it impossible to separate out the strands. With Big Data and powerful machine learning algorithms, most nonsensitive data give rise to inferences about sensitive data. In many privacy laws, data giving rise to inferences about sensitive data is also protected as sensitive data. Arguably, then, nearly all personal data can be sensitive, and the sensitive data categories can swallow up everything. As a result, most organizations are currently processing a vast amount of data in violation of the laws.
This Article argues that the problems with the sensitive data approach make it unworkable and counterproductive as well as expose a deeper flaw at the root of many privacy laws. These laws make a fundamental conceptual mistake—they embrace the idea that the nature of personal data is a sufficiently useful focal point for the law. But nothing meaningful for regulation can be determined solely by looking at the data itself. Data is what data does.
To be effective, privacy law must focus on harm and risk rather than on the nature of personal data. The implications of this point extend far beyond sensitive data provisions. In many elements of privacy laws, protections should be proportionate to the harm and risk involved with the data collection, use, and transfer.
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Professor Daniel J. Solove is a law professor at George Washington University Law School. Through his company, TeachPrivacy, he has created the largest library of computer-based privacy and data security training, with more than 150 courses. He is also the co-organizer of the Privacy + Security Forum events for privacy professionals.
