Pricing Private Data

34 Pages Posted: 16 Sep 2012 Last revised: 18 Nov 2012

See all articles by Vasilis Gkatzelis

Vasilis Gkatzelis

New York University (NYU) - Leonard N. Stern School of Business

Christina Aperjis

Hewlett-Packard Enterprise - Social Computing Lab

Bernardo A. Huberman

Stanford University

Date Written: September 14, 2012

Abstract

We consider a market where buyers can access unbiased samples of private data by appropriately compensating the individuals to whom the data corresponds (the sellers) according to their privacy attitudes. We show how bundling the buyers' demand can decrease the price that buyers have to pay per data point, while ensuring that sellers are willing to participate. Our approach leverages the inherently randomized nature of sampling, along with the risk-averse attitude of sellers in order to discover the minimum price at which buyers can obtain unbiased samples. We take a prior-free approach and introduce a mechanism that incentivizes each individual to truthfully report his preferences in terms of different payment schemes. We then show that our mechanism provides optimal price guarantees in several settings.

Keywords: private data, big data, ecommerce

JEL Classification: D49, C88, D82

Suggested Citation

Gkatzelis, Vasilis and Aperjis, Christina and Huberman, Bernardo A., Pricing Private Data (September 14, 2012). Available at SSRN: https://ssrn.com/abstract=2146966 or http://dx.doi.org/10.2139/ssrn.2146966

Vasilis Gkatzelis

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY
United States

Christina Aperjis

Hewlett-Packard Enterprise - Social Computing Lab ( email )

1501 Page Mill Road
Palo Alto, CA 9434
United States

Bernardo A. Huberman (Contact Author)

Stanford University ( email )

Palo Alto, CA 94305
United States

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