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Bounded-Loss Private Prediction Markets
Rafael Frongillo · Bo Waggoner

Tue Dec 04 02:00 PM -- 04:00 PM (PST) @ Room 210 #28

Prior work has investigated variations of prediction markets that preserve participants' (differential) privacy, which formed the basis of useful mechanisms for purchasing data for machine learning objectives. Such markets required potentially unlimited financial subsidy, however, making them impractical. In this work, we design an adaptively-growing prediction market with a bounded financial subsidy, while achieving privacy, incentives to produce accurate predictions, and precision in the sense that market prices are not heavily impacted by the added privacy-preserving noise. We briefly discuss how our mechanism can extend to the data-purchasing setting, and its relationship to traditional learning algorithms.

Author Information

Rafael Frongillo (CU Boulder)
Bo Waggoner (Microsoft)

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