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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >PURE: Blind Regression Modeling for Low Quality Data with Participatory Sensing
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PURE: Blind Regression Modeling for Low Quality Data with Participatory Sensing

机译：PURE：参与式感知的低质量数据盲回归建模

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摘要

Participatory regression modeling is a cost-efficient mechanism to establish the relationships among multiple dimensions of sensory data collected from volunteers. Getting an accurate model estimate is challenging for two main reasons. First, with the concern of confidentiality of individual private data, the original data are nearly unavailable; second, low quality data with outliers are inherently embedded in the collected data. In this paper, we propose an innovative scheme, PURE, which can accurately estimate the global regression model without the need for knowing local private data (referred to as ) even when there is a large portion of outliers embedded. The wisdom of PURE is to let individual participants peer judge and further improve the global estimate via negotiations. Meanwhile, during the whole process, all information is exchanged in an aggregated way. By design, PURE is secure and can well protect individual privacy. Furthermore, PURE is a lightweight protocol suitable for mobile devices. Extensive trace-driven simulation results show that PURE can achieve an outstanding accuracy gain of two orders of magnitude even with random outliers near a ratio of 50 percent compared with the state-of-the-art least square estimator.
机译：参与式回归建模是一种经济高效的机制，可以建立从志愿者那里收集的多个感官数据之间的关系。获得准确的模型估计值具有挑战性，主要有两个原因。首先，考虑到个人私人数据的机密性，原始数据几乎不可用。第二，具有离群值的低质量数据固有地嵌入到收集的数据中。在本文中，我们提出了一种创新方案PURE，即使在嵌入大量异常值的情况下，该方案也可以准确估计全局回归模型，而无需了解局部私有数据（称为）。 PURE的智慧在于让每个参与者进行同行评判，并通过谈判进一步提高全球估算。同时，在整个过程中，所有信息都以汇总的方式交换。通过设计，PURE是安全的，可以很好地保护个人隐私。此外，PURE是适用于移动设备的轻量级协议。大量跟踪驱动的仿真结果表明，与最先进的最小二乘估计器相比，即使随机离群值的比例接近50％，PURE也可以实现两个数量级的出色精度增益。

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