  Histograms and synthetic data are of key importance in data analysis.
However, researchers have shown that even aggregated data such as histograms,
containing no obvious sensitive attributes, can result in privacy leakage. To
enable data analysis, a strong notion of privacy is required to avoid risking
unintended privacy violations.
  Such a strong notion of privacy is differential privacy, a statistical notion
of privacy that makes privacy leakage quantifiable. The caveat regarding
differential privacy is that while it has strong guarantees for privacy,
privacy comes at a cost of accuracy. Despite this trade off being a central and
important issue in the adoption of differential privacy, there exists a gap in
the literature regarding providing an understanding of the trade off and how to
address it appropriately.
  Through a systematic literature review (SLR), we investigate the
state-of-the-art within accuracy improving differentially private algorithms
for histogram and synthetic data publishing. Our contribution is two-fold: 1)
we identify trends and connections in the contributions to the field of
differential privacy for histograms and synthetic data and 2) we provide an
understanding of the privacy/accuracy trade off challenge by crystallizing
different dimensions to accuracy improvement. Accordingly, we position and
visualize the ideas in relation to each other and external work, and
deconstruct each algorithm to examine the building blocks separately with the
aim of pinpointing which dimension of accuracy improvement each
technique/approach is targeting. Hence, this systematization of knowledge (SoK)
provides an understanding of in which dimensions and how accuracy improvement
can be pursued without sacrificing privacy.
