  This paper studies privacy in the context of complex decision support queries
composed of multiple conditions on different aggregate statistics combined
using disjunction and conjunction operators. Utility requirements for such
queries necessitate the need for private mechanisms that guarantee a bound on
the false negative and false positive errors. This paper formally defines
complex decision support queries and their accuracy requirements, and provides
algorithms that proportion the existing budget to optimally minimize privacy
loss while supporting a bounded guarantee on the accuracy. Our experimental
results on multiple real-life datasets show that our algorithms successfully
maintain such utility guarantees, while also minimizing privacy loss.
