论文标题
在非侵入性负载监控中保存隐私：差异隐私观点
Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective
论文作者
论文摘要
智能仪表设备可以更好地了解私人信息泄漏的潜在风险的需求。缓解这种风险的一种有希望的解决方案是将噪音注入仪表数据中，以达到一定程度的差异隐私。在本文中，我们在压缩传感框架中施放了一声非侵入载荷监控（NILM），并弥合了尼尔姆推理的理论准确性与差异隐私参数之间的差距。然后，我们得出有效的理论界限，以提供有关差异隐私参数如何影响尼尔姆性能的见解。此外，我们通过提出层次结构框架来解决多局尼尔姆问题，从而概括了我们的结论。数值实验验证了我们的分析结果，并在各种实际情况下提供了差异隐私的更好的物理见解。这也证明了我们工作对保留一般隐私机制设计的重要性。
Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this paper, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensing framework, and bridge the gap between theoretical accuracy of NILM inference and differential privacy's parameters. We then derive the valid theoretical bounds to offer insights on how the differential privacy parameters affect the NILM performance. Moreover, we generalize our conclusions by proposing the hierarchical framework to solve the multi-shot NILM problem. Numerical experiments verify our analytical results and offer better physical insights of differential privacy in various practical scenarios. This also demonstrates the significance of our work for the general privacy preserving mechanism design.
