The Possibilities and Limitations of Private Prediction Markets

Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan

Research output: Contribution to journalArticlepeer-review


We consider the design of private prediction markets, financial markets designed to elicit predictions about uncertain events without revealing too much information about market participants' actions or beliefs. Our goal is to design market mechanisms in which participants' trades or wagers influence the market's behavior in a way that leads to accurate predictions, yet no single participant has too much influence over what others are able to observe. We study the possibilities and limitations of such mechanisms using tools from differential privacy. We begin by designing a private one-shot wagering mechanism in which bettors specify a belief about the likelihood of a future event and a corresponding monetary wager. Wagers are redistributed among bettors in a way that more highly rewards those with accurate predictions. We provide a class of wagering mechanisms that are guaranteed to satisfy truthfulness, budget balance on expectation, and other desirable properties while additionally guaranteeing ϵ-joint differential privacy in the bettors' reported beliefs, and analyze the trade-off between the achievable level of privacy and the sensitivity of a bettor's payment to her own report. We then ask whether it is possible to obtain privacy in dynamic prediction markets, focusing our attention on the popular cost-function framework in which securities with payments linked to future events are bought and sold by an automated market maker. We show that under general conditions, it is impossible for such a market maker to simultaneously achieve bounded worst-case loss and ϵ-differential privacy without allowing the privacy guarantee to degrade extremely quickly as the number of trades grows (at least logarithmically in number of trades), making such markets impractical in settings in which privacy is valued. We conclude by suggesting several avenues for potentially circumventing this lower bound.

Original languageEnglish (US)
Article number3412348
JournalACM Transactions on Economics and Computation
Issue number3
StatePublished - Oct 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Statistics and Probability
  • Economics and Econometrics
  • Marketing
  • Computational Mathematics


  • Differential privacy
  • cost function market maker
  • prediction markets
  • wagering mechanisms


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