Energy resource control via privacy preserving data
Energy resource control via privacy preserving data
复制标题
通过隐私保护数据控制能源资源
DOI：
10.1016/j.epsr.2020.106719
复制
发表时间：
2020
影响因子：
3.9
通讯作者：
Rajagopal, Ram
中科院分区：
文献类型：
--
作者：
Chen, Xiao;Navidi, Thomas;Rajagopal, Ram
虽然智能电表的频繁监控可以实现对能源资源的精细控制，但它也增加了隐私信息泄露的风险，例如收入，家庭占用和功耗行为，这些信息可以由对手从数据中推断出来。我们提出了一种方法，释放修改后的智能电表数据，使特定的私人属性被掩盖，而在能源控制器中使用的数据的效用被保存。该方法通过注入以私有属性为条件的噪声来私有化数据，该噪声通过经由极小极大优化学习的线性滤波器来调节。优化包含分类器的损失函数的私人属性，我们最大化，和能源控制器的目标制定为一个规范形式的优化，我们最小化。我们在太阳能发电的家庭消费的聚合数据集和另一个来自能源监管委员会（CER）的数据集上进行了实验，该数据集包含具有敏感属性（如收入和房屋占用率）的家庭智能电表数据。我们在CER数据上证明，我们的方法能够降低对手将二进制收入标签分类为随机猜测的能力，同时将储能控制器的目标值保持在最佳值的10%以内。
Although the frequent monitoring of smart meters enables granular control over energy resources, it also increases the risk of leakage of private information such as income, home occupancy, and power consumption behavior that can be inferred from the data by an adversary. We propose a method of releasing modified smart meter data so specific private attributes are obscured while the utility of the data for use in an energy resource controller is preserved. The method privatizes data by injecting noise conditioned on the private attribute through a linear filter learned via a minimax optimization. The optimization contains the loss function of a classifier for the private attribute, which we maximize, and the energy resource controller’s objective formulated as a canonical form optimization, which we minimize. We perform our experiment on an aggregated dataset of household consumption with solar generation and another from the Commission for Energy Regulation (CER) that contains household smart meter data with sensitive attributes such as income and home occupancy. We demonstrate on the CER data that our method is able to reduce the ability of an adversary to classify a binary income label to that of random guessing while maintaining an objective value for an energy storage controller within 10% of optimal.