The Qlear Protocol’s main ingredient is its Multi-Party Computation network. The MPC network is what allows Qlear to perform trust-sensitive gaming functions outside of the control of the house, players, Qlear itself, or anyone else for that matter.
Instead of relying on one, trusted entity to compare hands, approve moves and select rightful winners, Qlear delegates this function to a network of strangers. These strangers have no knowledge about the games they help to process, nor the players participating in them. Hence, this distributed computation network functions as an “ideal virtual third party”, that physically can’t cheat or unfairly assist one of the involved parties.
To better understand the science that makes this possible, let’s start with the formal definition:
Multi-Party Computation, or Privacy-Preserving Computation, is a subfield of cryptography, seeking to allow multiple parties to process information jointly and to derive shared insights from their information pool, without having to reveal the information they contributed to said pool.
Or in simpler terms: “How can we compare our secrets without exposing them?”
Confusing as this statement may sound, it is neatly demonstrated by the thought experiment that heralded this research field in the early Eighties - The so-called “Millionaires' Problem” (or “Billionaires’ problem”, adjusted to inflation).
Let’s assume two Billionaires, Jeff and Bill, want to compare their wealth to determine the richest person in the world. However, Jeff and Bill don’t trust each other, and may even suspect that their rival might be an undercover IRS agent. Consequently, the two opponents can’t just open their books and compare holdings.
In a classical setting, our Billionaires’ best shot would be to reach out to a trusted and incorruptible third party. Let’s call him Elon. Elon would then inspect the billionaire's books on their behalf, calculate their respective worth discreetly, and announce a winner. Jeff, Bill, and the rest of the world would now have irrefutable proof regarding the identity of the richest one of all (it’s Jeff), without having any specific knowledge on his holdings. Problem solved.
Unfortunately, we live in a world in which neither Jeff, Bill, nor Elon can be blindly trusted. Math, on the other hand, can always be trusted.
The first cryptographic solution to the Billionaires’ problem, introduced by Andrew Yao in 1982, proposes a way in which Jeff and Bill can resolve their dispute amongst themselves, without relying on Elon’s good graces. Yao demonstrated that Jeff and Bill can exchange encrypted information describing their wealth, compare it, and reach consensus regarding its size, without ever having to reveal their actual holdings to anyone.
In recent years the solution to the Billionaires’ problem has been generalized to include an unlimited number of participants that can jointly compute almost any function over their inputs while keeping those inputs completely undisclosed. While the original Billionaire’s Problem poses the simple question: “which number is bigger”, modern MPC algorithms can answer a variety of inquiries, such as: “Who holds the winning hand?”, “Was Jeff’s move according to the rules?”, “Is Bill in Check-mate?”. Just as with Yao’s original solution, the players can achieve irrefutable proof without having to reveal their secrets to each other or to a third party.
In contrast to the original two-player Billionaires’ problem, modern MPC networks (like the model that Qlear’s framework is built on), are designed to be fault tolerant to various degrees. This means that the network is protected from malevolent participants that have joined for the purpose of compromising its calculations. Fraud attempts will be mathematically detected and excluded from the end result.
In Qlear’s specific case, MPC participants are connected via Plasma side-chains that allow for staking mechanisms to be employed. This means that an entity wishing to become a Qlear MPC node has to first submit a “security deposit”. If the party is caught cheating, their respective deposit would be confiscated, and the faulty input exposed and disregarded. This renders attacks futile and expensive, in turn providing better integrity than any centralized solution.
Beyond decentralized p2p gaming, this technique can also be applied in many additional fields, including privacy-preserving big data, medical research, and distributed computation. In future posts, we will dive a bit deeper and illustrate how Qlear’s work can be instrumental in these areas. So stay tuned here, follow us on Twitter, and join us on Discord.
The Qlear Team.