“Big data” can be defined as a problem-solving philosophy that leverages massive data-sets and algorithmic analysis to extract “hidden information and surprising correlations." Not only does big data pose a threat to traditional notions of privacy, but it also compromises socially shared information. This point remains under appreciated because our so-called public disclosures are not nearly as public as courts and policymakers have argued — at least, not yet. That is subject to change once big data becomes user friendly.
Most social disclosures and details of our everyday lives are meant to be known only to a select group of people. Until now, technological constraints have favored that norm, limiting the circle of communication by imposing transaction costs — which can range from effort to money — onto prying eyes. Unfortunately, big data threatens to erode these structural protections, and the common law, which is the traditional legal regime for helping individuals seek redress for privacy harms, has some catching up to do.
To make our case that the legal community is under-theorizing the effect big data will have on an individual’s socialization and day-to-day activities, we will proceed in four steps. First, we explain why big data presents a bigger threat to social relationships than privacy advocates acknowledge, and construct a vivid hypothetical case that illustrates how democratized big data can turn seemingly harmless disclosures into potent privacy problems. Second, we argue that the harm democratized big data can inflict is exacerbated by decreasing privacy protections of a special kind — ever-diminishing “obscurity.” Third, we show how central common law concepts might be threatened by eroding obscurity and the resulting difficulty individuals have gauging whether social disclosures in a big data context will sow the seeds of forthcoming injury. Finally, we suggest that one way to stop big data from causing big, un-redressed privacy problems is to update the common law with obscurity-sensitive considerations.'Big Data Proxies and Health Privacy Exceptionalism' by Nicolas Terry argues that
while “small data” rules protect conventional health care data (doing so exceptionally, if not exceptionally well), big data facilitates the creation of health data proxies that are relatively unprotected. As a result, the carefully constructed, appropriate, and necessary model of health data privacy will be eroded. Proxy data created outside the traditional space protected by extant health privacy models will end exceptionalism, reducing data protection to the very low levels applied to most other types of data. The article examines big data and its relationship with health care, including the data pools in play, and pays particular attention to three types of big data that lead to health proxies: “laundered” HIPAA data, patient-curated data, and medically-inflected data. It then reexamines health privacy exceptionalism across legislative and regulatory domains seeking to understand its level of “stickiness” when faced with big data. Finally the article examines some of the claims for big data in the health care space, taking the position that while increased data liquidity and big data processing may be good for health care they are less likely to benefit health privacy.Terry concludes -
There is little doubt how the big data industry and its customers wish any data privacy debate to proceed. In the words of a recent McKinsey report the collective mind-set about patient data needs to be shifted from “protect” to “share, with protections.” Yet these “protections” fall far short of what is necessary and what patients have come to expect from our history of health privacy exceptionalism. Indeed, some of the specific recommendations are antithetical to our current approach to health privacy. For example, the report suggests encouraging data sharing and streamlining consents, specifically that “data sharing could be made the default, rather than the exception.” However, McKinsey also noted the privacy-based objections that any such proposals would face:
[A]s data liquidity increases, physicians and manufacturers will be subject to increased scrutiny, which could result in lawsuits or other adverse consequences.We know that these issues are already generating much concern, since many stakeholders have told us that their fears about data release outweigh their hope of using the information to discover new opportunities.
Speaking at a June 2013 conference FTC Commissioner Julie Brill acknowledged that HIPAA was not the only regulated zone that was being side-stepped by big data as “new-fangled lending institutions that forgo traditional credit reports in favor of their own big-data-driven analyses culled from social networks and other online sources.” With specific regard to HIPAA privacy and, likely, data proxies the Commissioner lamented:
[W]hat damage is done to our individual sense of privacy and autonomy in a society in which information about some of the most sensitive aspects of our lives is available for analysts to examine without our knowledge or consent, and for anyone to buy if they are willing to pay the going price.
Indeed, when faced with the claims for big data, health privacy advocates will not be able to rely on status quo arguments and will need to sharpen their defense of health privacy exceptionalism, while demanding new upstream regulation to constrict the collection of data being used to create proxy health data and sidestep HIPAA. As persuasively argued by Beauchamp and Childress, “We owe respect in the sense of deference to persons’ autonomous wishes not to be observed, touched, intruded on, and the like. The right to authorize access is basic.”
Of course one approach to the issue is to shift our attention to reducing or removing the incentives for customers of predictive analytics firms to care about the data. Recall how Congress was sufficiently concerned about how health insurers would use genetic information to make individual underwriting decisions that it passed GINA, prohibiting them from acquiring such data. Yet, today some (but not all) arguments for such genetic privacy exceptionalism seem less urgent given that the ACA broadly requires guaranteed issue and renewability, broadly prohibiting pre-existing condition exclusions or related discrimination. A realistic long-term goal must be to reduce disparities and discrimination and thereby minimize any incentive to segment using data profiling.
A medium-term but realistic prediction is that there is a politically charged regulatory fight on the horizon. After all, as Mayer-Schonberger and Cukier note, “The history of the twentieth century [was] blood-soaked with situations in which data abetted ugly ends.” Disturbingly, however, privacy advocates may not like how that fight likely will turn out. Increasingly, as large swathes of the federal government become embroiled in and enamored with big data-driven decision-making and surveillance, so it may become politically or psychologically difficult for them to contemplate regulating mirroring behavior by private actors.
On the other hand the position that we should not be taken advantage of without our permission could gain traction resulting in calls such as expressed herein for increased data protection. Then we will need to enact new upstream data protection of broad applicability (i.e., without the narrow data custodian definitions we see in sector-based privacy models). Defeat of such reform will leave us huddled around downstream HIPAA protection, an exceptional protection, but increasingly one that is (in big data terms) too small to care about and that can be circumvented by proxy data produced by the latest technologies.