差异化专用智能计量：实施、分析和计费
Differentially Private Smart Metering: Implementation, Analytics, and Billing
摘要：
智能电网通过共享精细的用电数据来彻底改变配电，尽管同样的数据共享可以揭示大量关于用户的信息，并且对客户来说存在严重的隐私问题。在本文中，我们使用差分隐私来解决这些问题。差异隐私是一种隐私的统计概念，在共享敏感数据之前，它会在敏感数据中添加经过仔细校准的噪声，我们将其应用于为个人客户提供强大的隐私保证。在这种实现方式下，我们量化了隐私对智能电网分析的影响，这是公用事业公司感兴趣的，以量化隐私造成的准确性损失。同时，我们量化的财务影响，隐私，通过绑定偏差客户的水电费。仿真结果使用实际的功率使用数据，证明了这种方法在实践中的可行性。
英文摘要：
Smart power grids offer to revolutionize power distribution by sharing granular power usage data, though this same data sharing can reveal a great deal about users, and there are serious privacy concerns for customers. In this paper, we address these concerns using differential privacy. Differential privacy is a statistical notion of privacy that adds carefully-calibrated noise to sensitive data before sharing it, and we apply it to provide strong privacy guarantees to individual customers. Under this implementation, we quantify the impact of privacy upon smart grid analytics, which are of interest to utilities, to quantify the loss in accuracy incurred by privacy. Simultaneously, we quantify the financial impact of privacy through bounding deviations in customers' utility bills. Simulation results are provided using actual power usage data, demonstrating the viability of this approach in practice.
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