Competitive Privacy in the Smart Grid: An Information-theoretic Approach

TL;DR

This paper models the privacy-utility tradeoff in smart grid state estimation as an information-theoretic lossy source coding problem, revealing optimal communication strategies for RTOs to balance data sharing and privacy.

Contribution

It introduces an information-theoretic framework for the privacy-utility tradeoff in smart grid state estimation, providing a single-round communication solution for correlated RTO data.

Findings

Feasible utility-privacy pairs achieved with one communication round

Correlation-aware communication improves privacy-utility tradeoff

The lossy source coding formulation is of independent interest

Abstract

Advances in sensing and communication capabilities as well as power industry deregulation are driving the need for distributed state estimation in the smart grid at the level of the regional transmission organizations (RTOs). This leads to a new competitive privacy problem amongst the RTOs since there is a tension between sharing data to ensure network reliability (utility/benefit to all RTOs) and withholding data for profitability and privacy reasons. The resulting tradeoff between utility, quantified via fidelity of its state estimate at each RTO, and privacy, quantified via the leakage of the state of one RTO at other RTOs, is captured precisely using a lossy source coding problem formulation for a two RTO network. For a two-RTO model, it is shown that the set of all feasible utility-privacy pairs can be achieved via a single round of communication when each RTO communicates taking into account the correlation between the measured data at both RTOs. The lossy source coding problem and solution developed here is also of independent interest.