Big data analytics and the limits of privacy self-management

Abstract

This article looks at how 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. We argue that the predominant privacy protection regimes based on the privacy self-management framework of “notice and choice” not only fail to protect individual privacy, but also underplay privacy as a collective good. To illustrate this claim, we discuss how two possible individual strategies—withdrawal from the market (avoidance) and complete reliance on market-provided privacy protections (assimilation)—may result in less privacy options available to the society at large. We conclude by discussing how acknowledging the collective dimension of privacy could provide more meaningful alternatives for privacy protection.

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