In modern commerce, firms and platforms typically use customer features from different sources to personalize prices: some features are verified using seller records or third-party data, while others are self-reported and may be strategically manipulated by buyers seeking lower prices. In this paper, we study the problem of source-aware dynamic pricing with strategic buyers, where the buyer's valuation is a sparse linear function of her high-dimensional features plus market noise with an unknown distribution. We develop the Source-Aware Localized exploration and calibrated Exploitation (SALE) policy, which personalizes exploration using past learning and verified features, and corrects for strategic manipulation using verified and reported features during exploitation. Our novel personalized design aims to narrow the range of prices explored, reducing unnecessary exploration at excessively high or low prices. To support learning under this design, we derive a new population equation that enables sparse valuation parameter estimation and, in turn, nonparametric estimation of the unknown noise distribution, even when buyer features are dependent over time. By uncovering the intrinsic sparse structure, our analysis establishes estimation guarantees for both learning tasks using the same exploration data. With these guarantees, we show that SALE achieves a sublinear expected regret bound, explicitly quantifying the benefit of using verified features to narrow exploration intervals. Several numerical studies further demonstrate the appealing performance of SALE.

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