I suspect that you’ll need to model the rounding process that your participants are performing to convert their ‘true’ privacy valuation into the monetary amount that they report. Unfortunately, I don’t think rounding like this is possible in brms—you’re probably going to have to implement it in pure Stan.
The Stan User’s Guide has a section on rounding that explores two approaches: marginalization and latent variables. The toy example, however, applies the same rounding rule to all observations. Your case appears to be trickier because the response distribution suggests that participants may be using different rounding rules, both for direction (ceiling/floor/nearest) and precision of the reported value (e.g., 0.01, 0.10, 1.00, or 10.00?).
Hopefully others with domain expertise on pricing decisions can weigh in on the best strategy for these data. If not, the usual advice applies: start with as simple of a model as possible then slowly build it up to include more nuance/mechanism.