  We address practical implementation of a risk-weighted pseudo posterior
synthesizer for microdata dissemination with a new re-weighting strategy that
maximizes utility of released synthetic data under at any level of formal
privacy guarantee. Our re-weighting strategy applies to any vector-weighted
pseudo posterior mechanism under which a vector of observation-indexed weights
are used to downweight likelihood contributions for high disclosure risk
records. We demonstrate our method on two different vector-weighted schemes
that target high-risk records. Our new method for constructing record-indexed
downeighting maximizes the data utility under any privacy budget for the
vector-weighted synthesizers by adjusting the by-record weights, such that
their individual Lipschitz bounds approach the bound for the entire database.
Our method achieves an $(\epsilon = 2 \Delta_{\boldsymbol{\alpha}})-$asymptotic
differential privacy (aDP) guarantee, globally, over the space of databases. We
illustrate our methods using simulated highly skewed count data and compare the
results to a scalar-weighted synthesizer under the Exponential Mechanism (EM).
We also apply our methods to a sample of the Survey of Doctorate Recipients and
demonstrate the practicality of our methods.
