This article seems to be about adding noise to queries in order to remove identifying information. Another really nice approach is https://en.wikipedia.org/wik

This article seems to be about adding noise to queries in order to remove identifying information. Another really nice approach is https://en.wikipedia.org/wiki/Local_differential_privacy https://en.wikipedia.org/wiki/Local_differential_privacy which randomises the data before it's even recorded. A classic example is asking someone a sensitive question, like whether they use some illegal drug. To record the responses, we do the following: - Ask the question - Toss a coin - If heads, toss the coin again then write down the participant's answer - If tails, toss the coin again and write down 'yes' if it's heads or 'no' if it's tails This way, each participant has plausible deniability, since their record has a 50% chance of coming from a coin toss, regardless of whether it's 'yes' or 'no'. Yet we can still estimate the prevalence from this dataset, by taking into account that around 1/4 of responses are fake 'yes' and around 1/4 are fake 'no'. For example, if we see 75% no and 25% yes we know there is a low prevalence of 'yes'. Also note that we always toss the coin twice: hence a distant observer wouldn't know whether we've just written down a real or fake response.