Privacy-Preserving Power Flow Analysis via Secure Multi-Party Computation

TL;DR

This paper introduces a privacy-preserving method for power flow analysis in smart grids using secure multi-party computation, enabling data analysis without revealing private information, thus addressing privacy concerns in smart meter data.

Contribution

It presents a novel application of secure multi-party computation to perform power flow analysis, specifically implementing Newton's method in a privacy-preserving manner for smart grid data.

Findings

Secure multi-party computation can effectively preserve privacy in smart grid analysis.

The proposed method is secure under the universal composability framework.

Benchmarks show practical feasibility across various grid types and threat models.

Abstract

Smart grids feature a bidirectional flow of electricity and data, enhancing flexibility, efficiency, and reliability in increasingly volatile energy grids. However, data from smart meters can reveal sensitive private information. Consequently, the adoption of smart meters is often restricted via legal means and hampered by limited user acceptance. Since metering data is beneficial for fault-free grid operation, power management, and resource allocation, applying privacy-preserving techniques to smart metering data is an important research problem. This work addresses this by using secure multi-party computation (SMPC), allowing multiple parties to jointly evaluate functions of their private inputs without revealing the latter. Concretely, we show how to perform power flow analysis on cryptographically hidden prosumer data. More precisely, we present a tailored solution to the power flow problem building on an SMPC implementation of Newtons method. We analyze the security of our approach in the universal composability framework and provide benchmarks for various grid types, threat models, and solvers. Our results indicate that secure multi-party computation can be able to alleviate privacy issues in smart grids in certain applications.