Lightweight privacy for smart metering data by adding noise
Lightweight privacy for smart metering data by adding noise
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DOI：
10.1145/2554850.2554982
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发表时间：
2014-03
期刊：
影响因子：
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通讯作者：
Pedro Barbosa;Andrey Brito;H. Almeida;Sebastian Clauss
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文献类型：
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作者：
Pedro Barbosa;Andrey Brito;H. Almeida;Sebastian Clauss
借助智能计量基础设施，电力提供商有很多动机从消费者那里收集高分辨率的能源使用数据。然而，该收集包含有关受监控消费者的能源消耗的非常详细的信息。因此，需要解决一个严重的问题：如何保护消费者的隐私，同时使某些服务的提供仍然可行？显然，这是隐私和实用性之间的权衡。有多种方法可以保护隐私，但其中许多方法会影响数据的有用性或计算成本高昂。在本文中，我们提出并评估了一种基于添加噪声的轻量级隐私和实用方法。此外，使用真实消费者的数据，我们讨论了该技术在各种智能电网场景中的影响。最后，我们还设计和评估对我们的解决方案可能的攻击。
With a Smart Metering infrastructure, there are many motivations for power providers to collect high-resolution data of energy usage from consumers. However, this collection implies very detailed information about the energy consumption of consumers being monitored. Consequently, a serious issue needs to be addressed: how to preserve the privacy of consumers but making the provision of certain services still possible? Clearly, this is a tradeoff between privacy and utility. There are approaches for preserving privacy in various ways, but many of them affect the data usefulness or are computationally expensive. In this paper, we propose and evaluate a lightweight approach for privacy and utility based on the addition of noise. Furthermore, using real consumers' data, we discuss the influence of the technique in various Smart Grid scenarios. Finally, we also design and evaluate possible attacks to our solution.