Pricing private data

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. We take a prior-free approach and introduce mechanisms that incentivize each individual to truthfully report his preferences in terms of different payment schemes. We then show that our mechanisms provide optimal price guarantees in several settings.