Privacy and fidelity trade-off in small area estimation

Music City Center 

We consider the problem of area-level small area modeling, and consider the efficiency and accuracy of small area predictions when privacy guarantees are desired. We consider a Bayesian framework for this problem. Here, an adversary has some information about the data and has prior opinions about the rest of the data. The parameter estimates and the small area predictions are rendered confidential in such a way such that a balance is achieved between privacy guarantees and quality of the inferences.