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A Theory Of Pricing Private Data

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When the analysis of individuals' personal information has value to an institution, but it compromises privacy, should individuals be compensated? We describe the foundations of a market in which those seeking access to data must pay for it and individuals are compensated for the loss of privacy they may suffer.

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1. Introduction

The interests of individuals and institutions with respect to personal data are often at odds. Personal data has great value to institutions: they eagerly collect it and monetize it by using it to model customer behavior, personalize services, target advertisements, or by selling the data directly. Yet the inappropriate disclosure of personal data poses a risk to individuals. They may suffer a range of harms including elevated prices for goods or services, discrimination, or exclusion from employment opportunities.3

A rich literature on privacy-preserving data analysis4,6,11 has tried to devise technical means for negotiating these competing interests. The goal is to derive accurate aggregate information from data collected from a group of individuals while at the same time protecting each member's personal information. But this approach necessarily imposes restrictions on the use of data. A seminal result from this line of work is that any mechanism providing reasonable privacy must strictly limit the number of query answers that can be accurately released.5 Nevertheless, recent research into differential privacy,7 a formal model of privacy in which an individual's privacy loss is rigorously measured and bounded, has shown that, for some applications, accurate aggregate analysis need not entail significant disclosure about individuals. Practical adoption of these techniques is slowing increasing: they have been used in a U.S. Census product16 and for application monitoring by Google9 and Apple.13


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