[D] Practitioners of differential privacy - what’s the typical epsilon value you’ve used?

It’s hard to find what are typical or accepted ranges of epsilon for differential privacy. Online search has not yielded citable or consistent results. Some sources have said typical values of 1.0 to 10.0.

Personally I have found that:
1) differential privacy significantly slows down training speed by almost 10X
2) often I have to use epsilon figures of 1.0 - 5.0 for results to still have required levels of accuracy
3) it’s either I have to increase dataset size …

consistent differential differential privacy epsilon found machinelearning online search privacy results search speed training value values