Safe Sharing Sites

Lisa M. Autin & David Lie

In this Article, Lisa Austin and David Lie argue that data sharing is an activity that sits at the crossroads of privacy concerns and the broader challenges of data governance surrounding access and use. Using the Sidewalk Toronto “smart city” proposal as a starting point for discussion, we outline these concerns to include resistance to data monopolies, public control over data collected through the use of public infrastructure, public benefit from the generation of intellectual property, the desire to broadly share data for innovation in the public interest, social—rather than individual— surveillance and harms, and that data use be held to standards of fairness, justice, and accountability. Data sharing is sometimes the practice that generates these concerns and sometimes the practice that is involved in the solution to these concerns.

Their safe sharing site approach to data sharing focuses on resolving key risks associated with data sharing, including protecting the privacy and security of data subjects, but aims to do so in a manner that is independent of the various legal contexts of regulation and governance. Instead, we propose that safe sharing sites connect with these different contexts through a legal interface consisting of a registry that provides transparency in relation to key information that supports different forms of regulation. Safe sharing sites could also offer assurances and auditability regarding the data sharing, further supporting a range of regulatory interventions. It is therefore not an alternative to these interventions but an important tool that can enable effective regulation.

A central feature of a safe sharing site is that it offers an alternative to the strategy of de-identifying data and then releasing it, whether within an “open data” context or in a more controlled environment. In a safe sharing site, computations may be performed on the data in a secure and privacy-protective manner without releasing the raw data, and all data sharing is transparent and auditable. Transparency does not mean that all data sharing becomes a matter of “public” view, but rather that there is the ability to make these activities visible to organizations and regulators in appropriate circumstances while recognizing the potential confidentiality interests in data uses.

In this way, safe sharing sites facilitate data sharing in a manner that manages the complexities of sharing while reducing the risks and enabling a variety of forms of governance and regulation. As such, the safe sharing site offers a flexible and modular piece of legal-technical infrastructure for the new economy.

This paper was prepared for and presented at the NYU Law Review Symposium 2018 on “Data Law in a Global Digital Economy”. It was published by the NYU Law Review in Volume 94, Number 4 (October 2019), pp. 581-623.

The False Promise of Health Data Ownership

In recent years there have been increasing calls by patient advocates, health law scholars, and would-be data intermediaries to recognize personal property interests in individual health information (IHI). While the propertization of IHI appeals to notions of individual autonomy, privacy, and distributive justice, the implementation of a workable property system for IHI presents significant challenges. This Article addresses the issues surrounding the propertization of IHI from a property law perspective. It first observes that IHI does not fit recognized judicial criteria for recognition as personal property, as IHI defies convenient definition, is difficult to possess exclusively, and lacks justifications for exclusive control. Second, it argues that if IHI property were structured along the lines of traditional common law property, as suggested by some propertization advocates, prohibitive costs could be imposed on socially valuable research and public health activity and IHI itself could become mired in unanticipated administrative complexities. Third, it discusses potential limitations and exceptions on the scope, duration, and enforceability of IHI property, both borrowed from intellectual property law and created de novo for IHI.

Yet even with these limitations, inherent risks arise when a new form of property is created. When owners are given broad rights of control, subject only to enumerated exceptions that seek to mitigate the worst effects of that control, constitutional constraints on governmental takings make the subsequent refinement of those rights difficult if not impossible, especially when rights are distributed broadly across the entire population. Moreover, embedding a host of limitations and exceptions into a new property system simply to avoid the worst effects of propertization begs the question whether a property system is needed at all, particularly when existing contract, privacy, and anti-discrimination rules already exist to protect individual privacy and autonomy in this area. It may be that one of the principal results of propertizing IHI is enriching would-be data intermediaries with little net benefit to individuals or public health. This Article concludes by recommending that the propertization of IHI be rejected in favor of sensible governmental regulation of IHI research coupled with existing liability rules to compensate individuals for violations of their privacy and abusive conduct by data handlers.

Ideas contained in this paper were discussed during the roundtable on data ownership at the NYU Law Review Symposium 2018 on “Data Law in a Global Digital Economy”. The paper was published by the NYU Law Review in Volume 94, Number 4 (October 2019), pp. 624-661.

Contracting for Personal Data

Is contracting for the collection, use, and transfer of data like contracting for the sale of a horse or a car or licensing a piece of software? Many are concerned that conventional principles of contract law are inadequate when some consumers may not know or misperceive the full consequences of their transactions. Such concerns have led to proposals for reform that deviate significantly from general rules of contract law. However, the merits of these proposals rest in part on testable empirical claims. We explore some of these claims using a hand-collected data set of privacy policies that dictate the terms of the collection, use, transfer, and security of personal data. We explore the extent to which those terms differ across markets before and after the adoption of the General Data Protection Regulation (GDPR). We find that compliance with the GDPR varies across markets in intuitive ways, indicating that firms take advantage of the flexibility offered by a contractual approach even when they must also comply with mandatory rules. We also compare terms offered to more and less sophisticated subjects to see whether firms may exploit information barriers by offering less favorable terms to more vulnerable subjects.

This paper was prepared for and presented at the NYU Law Review Symposium 2018 on “Data Law in a Global Digital Economy”. It was published by the NYU Law Review in Volume 94, Number 4 (October 2019), pp. 662-705.

Machines as the New Oompa-Loompas: Trade Secrecy, the Cloud, Machine Learning, and Automation

In previous work, I wrote about how trade secrecy drives the plot of Roald Dahl’s novel Charlie and the Chocolate Factory, explaining how the Oompa-Loompas are the ideal solution to Willy Wonka’s competitive problems. Since publishing that piece I have been struck by the proliferating Oompa-Loompas in contemporary life: computing machines filled with software and fed on data. These computers, software, and data might not look like Oompa-Loompas, but they function as Wonka’s tribe does: holding their secrets tightly and internally for the businesses for which these machines are deployed.

Computing machines were not always such effective secret-keeping Oompa Loompas. As this Article describes, at least three recent shifts in the computing industry—cloud computing, the increasing primacy of data and machine learning, and automation—have turned these machines into the new Oompa-Loompas. While new technologies enabled this shift, trade secret law has played an important role here as well. Like other intellectual property rights, trade secret law has a body of built-in limitations to ensure that the incentives offered by the law’s protection do not become so great that they harm follow-on innovation—new innovation that builds on existing innovation—and competition. This Article argues that, in light of the technological shifts in computing, the incentives that trade secret law currently provides to develop these contemporary Oompa-Loompas are excessive in relation to their worrisome effects on follow-on innovation and competition by others. These technological shifts allow businesses to circumvent trade secret law’s central limitations, thereby overfortifying trade secrecy protection. The Article then addresses how trade secret law might be changed—by removing or diminishing its protection—to restore balance for the good of both competition and innovation.

Ideas contained in this paper were discussed during the roundtable on data ownership at the NYU Law Review Symposium 2018 on “Data Law in a Global Digital Economy”. The paper was published by the NYU Law Review in Volume 94, Number 4 (October 2019), pp. 706-736.