# Efficient Data Aggregation in Smart Grids: A Personalized Local Differential Privacy Scheme

## TL;DR

This paper introduces a new privacy scheme for smart grids that lets users choose their privacy level while maintaining accurate data analysis.

## Contribution

The novel EDAS-PLDP scheme enables personalized privacy levels and improves data aggregation accuracy in smart grids.

## Key findings

- EDAS-PLDP outperforms existing schemes in estimation accuracy across various privacy preferences and data scales.
- The scheme reduces time consumption and is robust against false data injection attacks.
- Mean square error-based aggregation optimizes global mean estimation for electricity consumption.

## Abstract

The rapid advancement of smart grids, while enhancing the efficiency of power systems, has also raised serious concerns regarding the privacy and security of end-users’ electricity consumption data. Traditional privacy protection methods struggle to meet users’ individualized privacy requirements and often lead to a significant decline in data aggregation accuracy. To address the core contradiction between personalized privacy protection and high-precision grid analytics, this paper proposes an efficient data aggregation scheme based on personalized local differential privacy (EDAS-PLDP) tailored for smart grids. The proposed scheme enables smart terminal users to autonomously select their privacy protection levels based on individual needs, thereby breaking the limitations of the traditional “one-size-fits-all” approach. To mitigate the accuracy loss caused by personalized perturbations, a mean square error-based weighted aggregation strategy is introduced at the gateway side. This strategy evaluates the data quality from groups with different privacy preferences and adjusts aggregation weights to optimize the estimation accuracy of the global mean electricity consumption. Extensive experimental results demonstrate that, compared to existing mainstream schemes, EDAS-PLDP achieves higher estimation accuracy under various distributions of privacy preferences, user scales, and data granularities, while exhibiting lower time consumption, making it suitable for resource-constrained smart grid environments. Furthermore, the scheme shows excellent robustness against false data injection attacks. In summary, EDAS-PLDP provides a balanced and efficient solution for reconciling personalized privacy protection with high-precision data utility in smart grids.