  Recently introduced privacy legislation has aimed to restrict and control the
amount of personal data published by companies and shared to third parties.
Much of this real data is not only sensitive requiring anonymization, but also
contains characteristic details from a variety of individuals. This diversity
is desirable in many applications ranging from Web search to drug and product
development. Unfortunately, data anonymization techniques have largely ignored
diversity in its published result. This inadvertently propagates underlying
bias in subsequent data analysis. We study the problem of finding a diverse
anonymized data instance where diversity is measured via a set of diversity
constraints. We formalize diversity constraints and study their foundations
such as implication and satisfiability. We show that determining the existence
of a diverse, anonymized instance can be done in PTIME, and we present a
clustering-based algorithm. We conduct extensive experiments using real and
synthetic data showing the effectiveness of our techniques, and improvement
over existing baselines. Our work aligns with recent trends towards responsible
data science by coupling diversity with privacy-preserving data publishing.
