  Privacy is an individual choice to determine which personal details can be
collected, used and shared. Individual consent and transparency are the core
tenets for earning customers trust and this motivates the organizations to
adopt privacy enhancing practices while creating the systems. The goal of a
privacy-aware design is to protect information in a way that does not increase
an adversary's existing knowledge about an individual beyond what is
permissible. This becomes critical when these data elements can be linked with
the wealth of auxiliary information available outside the system to identify an
individual. Privacy regulations around the world provide directives to protect
individual privacy but are generally complex and vague, making their
translation into actionable and technical privacy-friendly architectures
challenging. In this paper, we utilize Shannon's Entropy to create an objective
metric that can help simplify the state-of-the-art Privacy Design Strategies
proposed in the literature and aid our key technical design decisions to create
privacy aware architectures.
