Dobrushin Coefficients of Private Mechanisms Beyond Local Differential Privacy
The research investigates Dobrushin coefficients for discrete Markov kernels that satisfy Pointwise Maximal Leakage (PML), a privacy guarantee weaker than Local Differential Privacy (LDP). The study derives achievable bounds on contraction coefficients and extends results to general f-divergences, providing tighter privacy guarantees for mechanisms where strict LDP is too restrictive.