  An accountable algorithmic transparency report (ATR) should ideally
investigate the (a) transparency of the underlying algorithm, and (b) fairness
of the algorithmic decisions, and at the same time preserve data subjects'
privacy. However, a provably formal study of the impact to data subjects'
privacy caused by the utility of releasing an ATR (that investigates
transparency and fairness), is yet to be addressed in the literature. The
far-fetched benefit of such a study lies in the methodical characterization of
privacy-utility trade-offs for release of ATRs in public, and their
consequential application-specific impact on the dimensions of society,
politics, and economics. In this paper, we first investigate and demonstrate
potential privacy hazards brought on by the deployment of transparency and
fairness measures in released ATRs. To preserve data subjects' privacy, we then
propose a linear-time optimal-privacy scheme, built upon standard linear
fractional programming (LFP) theory, for announcing ATRs, subject to
constraints controlling the tolerance of privacy perturbation on the utility of
transparency schemes. Subsequently, we quantify the privacy-utility trade-offs
induced by our scheme, and analyze the impact of privacy perturbation on
fairness measures in ATRs. To the best of our knowledge, this is the first
analytical work that simultaneously addresses trade-offs between the triad of
privacy, utility, and fairness, applicable to algorithmic transparency reports.
