  Preserving the privacy of individuals by protecting their sensitive
attributes is an important consideration during microdata release. However, it
is equally important to preserve the quality or utility of the data for at
least some targeted workloads. We propose a novel framework for privacy
preservation based on the k-anonymity model that is ideally suited for
workloads that require preserving the probability distribution of the
quasi-identifier variables in the data. Our framework combines the principles
of distribution-preserving quantization and k-member clustering, and we
specialize it to two variants that respectively use intra-cluster and Gaussian
dithering of cluster centers to achieve distribution preservation. We perform
theoretical analysis of the proposed schemes in terms of distribution
preservation, and describe their utility in workloads such as covariate shift
and transfer learning where such a property is necessary. Using extensive
experiments on real-world Medical Expenditure Panel Survey data, we demonstrate
the merits of our algorithms over standard k-anonymization for a hallmark
health care application where an insurance company wishes to understand the
risk in entering a new market. Furthermore, by empirically quantifying the
reidentification risk, we also show that the proposed approaches indeed
maintain k-anonymity.
