In mobile crowd sensing (MCS) applications, a public model of a system is expected to be derived from observations collected by mobile device users, through regression modeling. For example, a model describing the relationship between running speed, heart rate, height, and weight of runner can be constructed using MCS data collected from wristbands. Unique features of MCS data bring regression new challenges. First, observations are error-prone and private, making it of great difficulty to derive an accurate model without acquiring raw data. Second, observations are nonstationary and opportunistically, calling for an adaptive model updating mechanism. Last, mobile devices are resource-constrained, posing an urgent demand for lightweight regression. We propose an adaptive and blind regression scheme. The core idea is first to select an optimal ‘safe’ subset of observations locally stored over all participants, such that the inconsistency between the subset and the corresponding regression model is minimized, and as many observations as possible are included. Then, based on the resulted regression model, more observations are checked and selected to refine the model. With observations constantly coming, newly selected ‘safe’ observations are used to make the model updated adaptively. To preserve data privacy, one-time pad masking and blocking scheme are integrated.

中文翻译：

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移动人群感知的自适应和盲回归

在移动人群感知 (MCS) 应用中，系统的公共模型预计将通过回归建模从移动设备用户收集的观察中推导出来。例如，可以使用从腕带收集的 MCS 数据构建描述跑步者跑步速度、心率、身高和体重之间关系的模型。MCS 数据的独特特征给回归带来了新的挑战。首先，观察很容易出错并且是私密的，这使得在不获取原始数据的情况下很难导出准确的模型。其次，观察是非平稳的和机会主义的，需要一种自适应模型更新机制。最后，移动设备资源受限，迫切需要轻量级回归。我们提出了一种自适应和盲回归方案。核心思想是首先选择一个最优的“安全”观察子集本地存储在所有参与者中，这样子集与相应回归模型之间的不一致被最小化，并包括尽可能多的观察。然后，基于得到的回归模型，检查并选择更多的观察值来改进模型。随着观测的不断到来，新选择的“安全”观测用于使模型自适应更新。为了保护数据隐私，集成了一次性填充屏蔽和阻塞方案。检查并选择更多的观察值以改进模型。随着观测的不断到来，新选择的“安全”观测用于使模型自适应更新。为了保护数据隐私，集成了一次性填充屏蔽和阻塞方案。检查并选择更多的观察值以改进模型。随着观测的不断到来，新选择的“安全”观测用于使模型自适应更新。为了保护数据隐私，集成了一次性填充屏蔽和阻塞方案。