Several peer consistency mechanisms have been proposed to incentivize agents for honestly solving crowdsourcing tasks. These game-theoretic mechanisms evaluate the answers provided by an agent based on the correlation with answers provided by other agents ("peers") who solve the same tasks. In this paper, we consider the problem of eliciting personal attributes (for e.g. body measurements) of the agents. Since attributes are personal in nature, the tasks can not be shared between two agents. We show for the first time how to extend a peer consistency incentive mechanism, the Logarithmic Peer Truth Serum, to this setting for collecting personal attributes. When individuals report combinations of multiple personal data attributes, the correlation between them can be exploited to find peers. This new mechanism applies, for example, to collecting personal health records and other multi-attribute measurements at private properties such as smart homes. We provide a theoretical analysis of the incentive properties of the new mechanism and show the performance of the mechanism on several public datasets, which confirm the theoretical analysis.