  Agencies, such as the U.S. Census Bureau, release data sets and statistics
about groups of individuals that are used as input to a number of critical
decision processes. To conform to privacy and confidentiality requirements,
these agencies are often required to release privacy-preserving versions of the
data. This paper studies the release of differentially private data sets and
analyzes their impact on some critical resource allocation tasks under a
fairness perspective. {The paper shows that, when the decisions take as input
differentially private data}, the noise added to achieve privacy
disproportionately impacts some groups over others. The paper analyzes the
reasons for these disproportionate impacts and proposes guidelines to mitigate
these effects. The proposed approaches are evaluated on critical decision
problems that use differentially private census data.
