Why privacy is a prerequisite for prediction markets

Prediction markets let users trade contracts on the outcomes of future events: elections, sports, policy decisions. Prices emerge from supply and demand, and because people are staking money rather than answering surveys, the prices tend to reflect what participants actually believe. During the 2024 US presidential election, 3.7 billion dollars was wagered on the outcome on Polymarket alone, and the platform passed 15 billion dollars in all-time volume. Where conventional polling missed, the market was close.

That accuracy is the product. And it depends on properties that a fully transparent blockchain quietly undermines.

The downsides of transparency

When every order is visible before it executes, the market's own mechanics become an attack surface:

• Front-running. Bots detect a pending position and insert their own order ahead of it, capturing the price move the original trader anticipated.
• Back-running. Bots queue directly behind a large visible trade to profit from its impact.
• Sandwich attacks. Both at once: trade ahead of the victim, let their order move the price, exit behind them. The victim pays the slippage.
• Transaction exclusion. A validator with a stake in an outcome can delay or suppress bets on the other side, distorting the price the market reports.

Transparency hurts participation too. A public bet is a public statement: an employee betting on their industry, an analyst betting against their published view, anyone in a jurisdiction where the topic is sensitive. When cautious participants stay out, liquidity thins and the market's forecast gets worse. The harm is not just to individual traders; it is to the accuracy that makes prediction markets worth having.

Why garbled circuits fit prediction markets

The fix is not to hide the market. Aggregate prices and outcomes should stay public; that is the point. What needs protection is the individual position before and after it lands.

Garbled-circuit MPC provides exactly this shape of privacy. Orders are encrypted client-side and the matching computation runs directly on ciphertext: observers see only encrypted data, and at no point in the process is plaintext exposed, not to bots, not to validators, not to the operator. Unlike zero-knowledge approaches, the computation can span many participants' private inputs at once, which is precisely what an order book or an auction requires. And it is efficient enough on ordinary hardware to make sense for small positions, not only whales.

The result is a prediction market where the aggregate signal is public, the individual conviction is private, and the manipulation tactics listed above have nothing to read. Better privacy is not in tension with market accuracy. It is what makes the accuracy trustworthy.

Read more about how Bubble works, or explore the privacy landscape.