  The increasing prevalence of large-scale data collection in modern society
represents a potential threat to individual privacy. Addressing this threat,
for example through privacy-enhancing technologies (PETs), requires a rigorous
definition of what exactly is being protected, that is, of privacy itself. In
this work, we formulate an axiomatic definition of privacy based on
quantifiable and irreducible information flows. Our definition synthesizes
prior work from the domain of social science with a contemporary understanding
of PETs such as differential privacy (DP). Our work highlights the fact that
the inevitable difficulties of protecting privacy in practice are fundamentally
information-theoretic. Moreover, it enables quantitative reasoning about PETs
based on what they are protecting, thus fostering objective policy discourse
about their societal implementation.
