The Influence of Differential Privacy on Short Term Electric Load Forecasting
This addresses privacy concerns in smart metering for households and energy providers, but it is incremental as it builds on existing differential privacy methods in a specific domain.
The paper tackles the problem of applying differential privacy to short-term electric load forecasting, showing that it enables privacy-preserving optimization with good utility for energy providers, particularly under linear regression, and maintains individual re-identification risk for households at less than 60%, only 10% above random guessing.
There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to billing at the energy provider. However, exploitation is mostly limited to application of cryptographic security means between smart meters and energy providers. We illustrate along the use case of privacy preserving load forecasting that Differential Privacy is indeed a valuable addition that unlocks novel information flows for optimization. We show that (i) there are large differences in utility along three selected forecasting methods, (ii) energy providers can enjoy good utility especially under the linear regression benchmark model, and (iii) households can participate in privacy preserving load forecasting with an individual re-identification risk < 60%, only 10% over random guessing.