Unwrapping the concept of zero-knowledge proofs

Conceptually, a zero-knowledge proof is similar to a randomized response study. Researchers are understandably concerned about whether people will honestly answer a question about a taboo behavior—such as drug use or interactions with sex workers.

In order to eliminate bias, statisticians came up with a method that introduced randomness in individual responses while keeping the meaning of overall results. Imagine you're trying to determine the prevalence of abortion by interviewing women, in a jurisdiction in which abortion is illegal. Have the interviewee flip a coin. If heads, answer the question honestly. If tails, just say Yes.

The researcher doesn't need to know the results of the coin flip, or each individual's true response—they only need to know that, given a sufficient sample size, taking the margin above 50% and doubling it gives you the actual prevalence of the practice. This approach preserves the privacy of the individual respondents while not compromising the quality of the data.

Zero-knowledge proofs (and arguments) are highly technical and the nuts and bolts are beyond the scope of this publication, but they are conceptually similar to what we're discussing. Depending on the specific implementation, zero knowledge might allow a user to spend the contents of their wallet without other users of the network knowing the contents of the wallet. This is one respect in which a cryptocurrency can truly be similar to cash: short of a mugger taking your wallet, there's no way for another person to know what you have until you've revealed it. This approach succeeds in addressing one of the largest problems in cryptocurrencies such as Bitcoin, and at the time of writing, Ethereum.