Rao Differential Privacy
This work offers a novel approach to differential privacy that could enhance privacy guarantees in data analysis, though it appears incremental as it builds on existing concepts.
The authors tackled the problem of defining differential privacy by proposing a new definition based on Rao distance from information geometry, which shares the interpretation of previous definitions while improving sequential composition results.
Differential privacy (DP) has recently emerged as a definition of privacy to release private estimates. DP calibrates noise to be on the order of an individuals contribution. Due to the this calibration a private estimate obscures any individual while preserving the utility of the estimate. Since the original definition, many alternate definitions have been proposed. These alternates have been proposed for various reasons including improvements on composition results, relaxations, and formalizations. Nevertheless, thus far nearly all definitions of privacy have used a divergence of densities as the basis of the definition. In this paper we take an information geometry perspective towards differential privacy. Specifically, rather than define privacy via a divergence, we define privacy via the Rao distance. We show that our proposed definition of privacy shares the interpretation of previous definitions of privacy while improving on sequential composition.