Dynamical Privacy in Distributed Computing -- Part I: Privacy Loss and PPSC Mechanism
For researchers and practitioners in distributed computing and privacy, this work systematically characterizes computational privacy risks and provides a generic encryption subroutine, though the evaluation is limited to linear solvers.
This paper identifies privacy risks in distributed computing protocols where eavesdroppers can infer local datasets from node states, and proposes a Privacy-Preserving-Summation-Consistent (PPSC) mechanism that leverages the consensus manifold to hide privacy while maintaining computational accuracy. The mechanism is demonstrated on distributed linear equation solvers.
A distributed computing protocol consists of three components: (i) Data Localization: a network-wide dataset is decomposed into local datasets separately preserved at a network of nodes; (ii) Node Communication: the nodes hold individual dynamical states and communicate with the neighbors about these dynamical states; (iii) Local Computation: state recursions are computed at each individual node. Information about the local datasets enters the computation process through the node-to-node communication and the local computations, which may be leaked to dynamics eavesdroppers having access to global or local node states. In this paper, we systematically investigate this potential computational privacy risks in distributed computing protocols in the form of structured system identification, and then propose and thoroughly analyze a Privacy-Preserving-Summation-Consistent (PPSC) mechanism as a generic privacy encryption subroutine for consensus-based distributed computations. The central idea is that the consensus manifold is where we can both hide node privacy and achieve computational accuracy. In this first part of the paper, we demonstrate the computational privacy risks in distributed algorithms against dynamics eavesdroppers and particularly in distributed linear equation solvers, and then propose the PPSC mechanism and illustrate its usefulness.