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Poster
A Market Framework for Eliciting Private Data
Bo Waggoner · Rafael Frongillo · Jacob D Abernethy

Thu Dec 10 08:00 AM -- 12:00 PM (PST) @ 210 C #56 #None
in Poster Session »

We propose a mechanism for purchasing information from a sequence of participants.The participants may simply hold data points they wish to sell, or may have more sophisticated information; either way, they are incentivized to participate as long as they believe their data points are representative or their information will improve the mechanism's future prediction on a test set.The mechanism, which draws on the principles of prediction markets, has a bounded budget and minimizes generalization error for Bregman divergence loss functions.We then show how to modify this mechanism to preserve the privacy of participants' information: At any given time, the current prices and predictions of the mechanism reveal almost no information about any one participant, yet in total over all participants, information is accurately aggregated.

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Author Information

Bo Waggoner (Harvard)
Rafael Frongillo (CU Boulder)
Jacob D Abernethy (University of Michigan)

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