Journal article
Pricing private data
Electronic markets, v 25(2), pp 109-123
01 Jun 2015
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.
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Details
- Title
- Pricing private data
- Creators
- Vasilis Gkatzelis - Palo Alto UniversityChristina Aperjis - Power Auct, Washington, DC 20007 USABernardo A. Huberman - Hewlett-Packard
- Publication Details
- Electronic markets, v 25(2), pp 109-123
- Publisher
- Springer Nature
- Number of pages
- 15
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000355341500004
- Scopus ID
- 2-s2.0-84930090029
- Other Identifier
- 991021868725804721
InCites Highlights
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Business
- Management