Increasing user controllability on device specific privacy in the Internet of Things
Increasing user controllability on device specific privacy in the Internet of Things
复制标题
提高物联网中用户对设备特定隐私的可控性
DOI：
10.1016/j.comcom.2017.11.009
复制
发表时间：
2018
影响因子：
6
通讯作者：
Asif W
中科院分区：
文献类型：
--
作者：
Asif W
随着近年来信息技术的进步，越来越多的设备被集成到物联网中。这些设备收集了大量与用户相关的私人信息，虽然在某些情况下它有助于改善个人的生活方式，但在其他情况下，它会引发重大的隐私问题。效用和隐私之间的这种权衡高度依赖于所考虑的设备，并且随着所生成数据的效用的增加，个人的隐私降低。在这篇文章中，我们制定了一个效用和隐私的权衡，使用户能够做出特定于设备的决定，即可以共享多少数据。这是通过将每个设备允许的隐私程度参数化并使用户能够配置每个设备的参数来实现的。我们使用智能计量应用程序作为所提出方法的测试用例场景。我们通过在ECO数据集上进行的模拟来评估它的性能。实验结果表明，该方法对家电的识别准确率为81.8%，准确率为70.1%。此外，还证明了设备特定配置参数的改变可以很好地控制特定设备和公用事业实现的隐私程度，从而证明了所提出的方法的有效性。此外，研究表明，正如预期的那样，功耗较高的设备对总体隐私和实现的效用贡献更大。在测试Weiss和Baranski的算法后，本文还进行了比较研究，结果表明，该方法的性能优于现有的电子隐私方法，因为它产生了更难识别的跟踪，这两种算法都是众所周知的非侵入式负载监控算法。最后，作为该方法的一个组成部分，噪声的加入可以极大地提高性能。
With recent advancements in information technology more and more devices are integrated in the Internet of Things. These devices gather significant amount of private information pertinent to a user and while, in some cases it helps in improving the life style of an individual, in others it raises major privacy concerns. This trade-off between utility and privacy is highly dependent upon the devices in consideration and as the utility of the generated data increases, the privacy of an individual decreases. In this paper, we formulate a utility-privacy trade-off that enables a user to make appliance specific decisions as to how much data can be shared. This is achieved by parametrizing the degree of privacy allowed for each device and enabling the user to configure the parameter of each device. We use the smart metering application as the test case scenario for the proposed approach. We evaluate its performance using simulations conducted on the ECO data set. Our results indicate that, the proposed approach is successful in identifying appliances with an accuracy of 81.8% and a precision of 70.1%. In addition, it is demonstrated that device specific changes of the configuration parameters allow the degree of privacy achieved for the particular device and the utility to be well controlled, thus demonstrating the effectiveness of the proposed approach. Moreover, it is shown that, as expected, devices with higher power consumption contribute more to the overall privacy and utility achieved. A comparative study is also conducted and the proposed approach is shown to outperform the existing ElecPrivacy approach by producing a trace that is harder to identify, as reported after testing the Weiss’ and Baranski’s algorithm, both of which are well known Non-Intrusive Load Monitoring algorithms. Finally, it is demonstrated that the addition of noise, which is an integral part of the propose approach, can greatly improve performance.
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