I'm looking at the Apple differential privacy document here and it has the paragraph:
The noise injection step works as follows: After encoding the input as a vector using a hash function, each coordinate of the vector is then flipped (written as an incorrect value) with a probability of 1/(1 + 𝑒^𝜀/2), where 𝜀 is the privacy parameter. This assures that analysis of the collected data cannot distinguish actual values from flipped values, helping to assure the privacy of the shared information.
I would like to experiment with hashing/obscuring my data with similar properties and have data bins that would look like this, but for different types of data.
How would I implement (or emulate) either the Count Mean Sketch or Hadamard Count Mean Sketch for my own purposes?