Abstract
The collection of detailed consumption data through smart metering has led to privacy concerns. Aggregating the consumption data over a number of smart meters can be used to strike a balance between functional and privacy requirements. A number of contributions have proposed the use of differential privacy in smart metering to perturb aggregates in order to provide a proven privacy property for end consumers. However, as differential privacy has originally been proposed for very large datasets, the applicability in real-world smart metering is not guaranteed. In this paper, the effect of differential privacy on real smart metering data is studied, especially with respect to balancing utility and privacy requirements. The main finding is that even after some improvements of the basic method the aggregation group size must be of the order of thousands of smart meters in order to have reasonable utility.
Highlights
Smart Grids introduce state-of-the-art information and communication technologies in energy grids to facilitate communication between grid participants, e.g., to enable widespread integration of distributed renewable energy sources, and toThe aggregated data will be more or less useful for other stakeholders in the energy grid, such as the distribution system operator (DSO), depending on the extent of spatial aggregation and the intended use case
For illustrative purpose we explicitly show here that smoothing does not destroy the differential privacy property
The analysis shows that differential privacy is not destroyed for a single, but arbitrary time point t and a simple moving average filter with span 3 is used
Summary
The aggregated data will be more or less useful for other stakeholders in the energy grid, such as the distribution system operator (DSO), depending on the extent of spatial aggregation and the intended use case. For use cases such as usage prognosis, network planning and settlement, the total, aggregated consumption of a set of N smart meters can be useful for moderate values of N [15]. While the aggregate value contains less (private) information, the aggregate value can still contain private information, there is no guarantee that the resulting aggregate value ensures privacy This holds even more for a daily profile of aggregate values. Spatially aggregated over N smart meters, the goal of this paper is to practically make this aggregated time profile privacy preserving
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