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Consumer Privacy on Distributed Energy Markets

机译:消费者隐私对分布式能源市场

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Recently, several privacy-enhancing technologies for smart grids have been proposed. However, most of these solutions presume the cooperation of all smart grid participants. Hence, the privacy protection of consumers depends on the willingness of the suppliers to deploy privacy-enhancing technologies. Since electrical energy is essential for our modern life, it is impossible for consumers to opt out. We propose a novel consumer-only (do-it-yourself) privacy-enhancing approach under the assumption that users can obtain their energy from multiple suppliers on a distributed market. By splitting the demand over multiple suppliers, the information each of them can collect about a single consumer is reduced. In this context, we suggest two different buying strategies: a time and a sample diversification strategy. To measure their provided level of privacy protection, we introduce a new indistinguishability metric λ-Indistinguishability (λ-IND) that measures how relative consumption changes can be hidden in the total consumption. We evaluate the presented strategies with λ-IND and derive first privacy boundaries. The evaluation of our buying strategies on real-world energy data sets indicates their ability to hide load profiles of privacy sensitive appliances at low communication and computational overhead.
机译:最近,已经提出了几种用于智能电网的隐私增强技术。但是,这些解决方案中的大多数都概述了所有智能电网参与者的合作。因此,消费者的隐私保护取决于供应商部署隐私增强技术的意愿。由于电能对我们的现代生活至关重要,因此消费者不可能选择退出。我们提出了一种新的消费者 - 仅限于(DO-IT-SOWN)隐私增强方法,在假设用户可以从分布式市场上的多个供应商处获得能量。通过对多个供应商分割需求,减少了每个人可以收集的信息减少。在这种情况下,我们建议两种不同的购买策略:时间和样本多样化策略。为了衡量其提供的隐私保护级别,我们介绍了一种新的无法区分度量标准λ-indistinguisty(λ-ind),以衡量相对消耗的变化如何隐藏在总消耗中。我们评估了λ-ind的策略并获得了第一个隐私界限。对现实能源数据集的购买策略的评估表明他们在低通信和计算开销下隐藏隐私敏感设备的负载型材的能力。

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