A Market Framework for Eliciting Private Data
Bo Waggoner, Rafael Frongillo, Jacob D. Abernethy
Abstract
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.
BibTeX
@inproceedings{NIPS2015_7af6266c,
author = {Waggoner, Bo and Frongillo, Rafael and Abernethy, Jacob D},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {A Market Framework for Eliciting Private Data},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/7af6266cc52234b5aa339b16695f7fc4-Paper.pdf},
volume = {28},
year = {2015}
}