Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation
This work addresses exact optimality in privacy-utility tradeoffs for distributed systems, representing an incremental advance over prior order-optimal methods.
The paper tackles the problem of distributed mean estimation under communication and local differential privacy constraints, achieving exact optimality in non-asymptotic settings by proving conditions for optimality and proposing a mechanism based on a randomly rotated simplex codebook.
We study the mean estimation problem under communication and local differential privacy constraints. While previous work has proposed \emph{order}-optimal algorithms for the same problem (i.e., asymptotically optimal as we spend more bits), \emph{exact} optimality (in the non-asymptotic setting) still has not been achieved. In this work, we take a step towards characterizing the \emph{exact}-optimal approach in the presence of shared randomness (a random variable shared between the server and the user) and identify several conditions for \emph{exact} optimality. We prove that one of the conditions is to utilize a rotationally symmetric shared random codebook. Based on this, we propose a randomization mechanism where the codebook is a randomly rotated simplex -- satisfying the properties of the \emph{exact}-optimal codebook. The proposed mechanism is based on a $k$-closest encoding which we prove to be \emph{exact}-optimal for the randomly rotated simplex codebook.