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The Glass House Effect: Big Data, the New Oil, and the Power of Analogy

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TLDR
This article draws on environmental law strategies to identify laws and policies that can protect privacy in the era of Big Data and develops a strategy for promoting technologies that will allow us to achieve the many benefits of Big data, while reducing its harmful privacy impacts.
Abstract
It is increasingly said that "Big Data is the new oil." The phrase has an optimistic ring to it. Big Data has many productive uses, just like oil. It will be a key resource for the information economy, just as oil has been for the smokestack economy. This article examines the underside of the comparison. Oil certainly has many productive uses, but it also leads to oil pollution. Big Data is similar. It produces tremendous benefits, but simultaneously generates significant privacy injuries. Environmental law has developed ways to reduce oil pollution. This article draws on these environmental law strategies to identify laws and policies that can protect privacy in the era of Big Data. Oil pollutes in two principal ways. It spills, and so despoils beaches, coastlines and waters. It also produces carbon emissions and so contributes to the greenhouse effect and climate change. Big Data creates analogous privacy injuries. Like oil, it spills. Data security breaches cause broad harm much as oil spills create wide-spread damage. Big Data’s privacy impacts are also analogous to carbon emissions. Just as oil combustion adds to the growing accumulation of greenhouse gases, so the producers of Big Data are generating layer upon layer of personal information. This build-up, too, creates a kind of heat. It increases the hot glare of public scrutiny and so makes the social environment a less hospitable place for the development of the human personality. This is not the greenhouse effect, but the glass house effect since it gives each of us the sense that we are living in a glass house. Society has developed legal and policy solutions to oil spills and to climate change. The second part of this article takes these measures, translates them into the privacy realm, and so generates ideas about how to reduce Big Data’s privacy impacts. It explains how the 1970 Clean Water Act and the 1990 Oil Pollution Act succeeded in reducing oil spills. Based on these strategies, it produces a set of legal and policy recommendations for decreasing data spills. It then turns to climate change policy — particularly laws and policies designed to promote clean energy innovation. Drawing on these initiatives, it develops a strategy for promoting technologies that will allow us to achieve the many benefits of Big Data, while reducing its harmful privacy impacts.

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References
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United States V. _____

TL;DR: The case of Nitokalisi Fonua (hereinafter, "Nick") as mentioned in this paper, who admitted to stealing a white GMC Blazer from a motel room at the Days Inn in Utah.
Journal ArticleDOI

Big data analytics: Computational intelligence techniques and application areas

TL;DR: This paper presents a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM).
Journal ArticleDOI

Big data analytics and the limits of privacy self-management

TL;DR: The logic of big data analytics, which promotes an aura of unchallenged objectivity to the algorithmic analysis of quantitative data, preempts individuals’ ability to self-define and closes off any opportunity for those inferences to be challenged or resisted.
Journal ArticleDOI

Big Data analytics and Computational Intelligence for Cyber–Physical Systems: Recent trends and state of the art applications

TL;DR: A comprehensive survey of computational intelligence techniques appropriate for the effective processing and analysis of big data and a novel biologically inspired universal generative modelling approach called Hierarchical Spatial–Temporal State Machine (HSTSM).