This paper answers these questions:
In short, (1) Noisy counts can be rounded. Another option is to use the Geometric distribution to noise in the form of integers. Another option is to leave unrounded, and make inferences (estimate functions of the data) with the noisy, floating point values for each bin. (2) Can replace negatives with zeros, or leave as negative.
From Section 2.1:
Because the noise can be negative, M0
can contain negative entries. In many applications, it is not meaningful to
have negative counts. However, we can adjust for this, e.g., by rounding negative values up to the smallest meaningful
value, 0. Let M0
+ denote the anonymized table obtained from M0 via this procedure. For small range queries or for
point queries, using M0
+ tends to be more accurate than M0
. However, since the noise is symmetric, retaining negative
values is useful for large queries which touch many entries, since the noise cancels. More precisely, the sum of noise
values is zero in expectation (but has non-trivial variance).