Today's cyber-physical systems (CPS) increasingly operate in social spaces. Examples include transportation systems, disaster response systems, and the smart grid, where humans are the drivers, survivors, or users. Much information about the evolving system can be collected from humans in the loop, a practice that is often called crowd-sensing. Crowd-sensing has not traditionally been considered a CPS topic, largely due to the difficulty in rigorously assessing its reliability. This paper aims to change that status quo by developing a mathematical approach for quantitatively assessing the probability of correctness of collected observations (about an evolving physical system), when the observations are reported by sources whose reliability is unknown. The paper extends prior literature on state estimation from noisy inputs, that often assumed unreliable sources that fall into one or a small number of categories, each with the same (possibly unknown) background noise distribution. In contrast, in the case of crowd-sensing, not only do we assume that the error distribution is unknown but also that each (human) sensor has its own possibly different error distribution. Given the above assumptions, we rigorously estimate data reliability in crowd-sensing systems, hence enabling their exploitation as state estimators in CPS feedback loops. We first consider applications where state is described by a number of binary variables, then extend the approach trivially to multivalued variables. The approach also extends prior work that addressed the problem in the special case of systems whose state does not change over time. Evaluation results, using both simulation and a real-life case-study, demonstrate the accuracy of the approach.