基于尺度摄动的智能计量系统隐私保护方案

Xuebin Ren, Xinyu Yang, Jie Lin, Qingyu Yang, Wei Yu
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引用次数: 12

摘要

智能电网对消费者隐私的暴露提出了很大的关注,因为智能计量系统中的细粒度测量可以通过披露准确的家庭用电负荷概况来暴露消费者的隐私。为了解决这个问题,在本文中,我们提出了新的基于尺度摄动的隐私保护方案,该方案可以以隐私友好和经济有效的方式实现对细粒度测量的巨大效用。我们的方案采用基于测量的尺度摄动,以低成本隐藏原始测量值。通过广泛的理论分析和实验相结合,我们的研究结果表明,与现有方案相比,所提出的方案可以通过细粒度的测量来保护消费者的隐私,并实现更好的效用-隐私权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Scaling Perturbation Based Privacy-Preserving Schemes in Smart Metering Systems
The smart grid poses great concern about the exposure of consumers' privacy as the fine-grained measurements in the smart metering system can expose consumer's privacy through the disclosure of accurate load profiles of home energy usage. To address this issue, in this paper we propose novel scaling perturbation based privacy-preserving schemes that can achieve great utility for fine-grained measurements in a privacy-friendly and cost-effective manner. Our schemes adopt the measurement-based scaling perturbation to hide original measurements with low cost. Through a combination of both extensive theoretical analysis and experiments, our results show that the proposed schemes can preserve consumers' privacy through fine-grained measurements and achieve a better utility-privacy tradeoff in comparison with the existing schemes.
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