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The influence of differential privacy on short term electric load forecasting

机译：差异隐私对短期电力负荷预测的影响

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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 membership inference risk 60%, only 10% over random guessing.
机译：带有差异性隐私保护功能的智能电表已经做出了大量贡献，解决了从智能电表的实际执行到能源提供商的计费的问题。但是，开发主要限于在智能电表和能源提供商之间应用密码安全手段。我们沿用隐私保护负载预测的用例说明，差异隐私确实是有价值的补充，可以解锁新颖的信息流以进行优化。我们表明（i）三种选择的预测方法在效用方面存在很大差异;（ii）能源提供商尤其在线性回归基准模型下可以享受良好的效用;并且（iii）家庭可以与个人一起参与隐私保护负荷预测成员推理风险<60％，仅比随机猜测低10％。

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