The Census Bureau is using a new technique to keep people’s information more secure. But that comes at the expense of accuracy.
Implementing a simple differentially private algorithm for calculating a count, for example, requires setting a privacy loss budget, “epsilon.” If we choose a higher epsilon value, we expect the algorithm to add less noise to the count. In turn, privacy protections won’t be as strong. As an approach, differential privacy is agnostic about how to both choose an overall epsilon value and allocate it across released statistics.
” between accuracy and privacy is the goal, doing so is far from trivial and requires considering a few things about how differential privacy works.First, statistics for smaller populations may be relatively less accurate compared with statistics for larger populations. To get the same privacy guarantees, we must add more noise to smaller datasets.
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