A group of four friends—Alice, Ben, Chloe, and Stoffel—must decide where to dine without exposing personal budgets or preferences. Each submits a pair of 0‑10 scores: affordability and food preference. The goal is to compute a combined restaurant score while keeping every individual input secret in a privacy‑respecting protocol that relies on mathematical guarantees today.

The solution hinges on secret sharing, a technique that splits each score into two random shares. Only when at least two friends combine their shares can the true value be reconstructed. By applying this to both affordability and preference scores, the group can privately calculate per‑restaurant totals without revealing details to everyone else.

To multiply shared secrets without raising the reconstruction threshold, researchers use Beaver Triples. Each triple consists of two random masks and their product, all shared among participants. By revealing the difference between a share and its mask, the protocol keeps the underlying values hidden while enabling efficient multiplication of secret shares in privacy‑preserving computations.

A service now distributes Beaver Triples generated from satellite entropy, ensuring high‑quality randomness. Every friend receives identical triples, allowing them to perform secure multiplications locally. When two participants open the final restaurant scores, the group learns only the aggregate values, preserving individual privacy while still enabling a consensus choice after a brief calculation today.