In this paper, I separately measure two motives for consumers to protect privacy: an intrinsic motive, which is a “taste” for privacy; and an instrumental motive, which reflects the expected economic loss from revealing one’s private information to the firm. While the intrinsic preference is a utility primitive, the instrumental preference arises endogenously from a firm’s usage of consumer data. Combining a two-stage experiment and a structural model, I find that consumers’ intrinsic preferences for privacy range from 0 to 5 dollars per demographic variable, exhibiting substantial heterogeneity across consumers and categories of personal data. This rich heterogeneity in intrinsic preferences leads to a selection pattern that deviates from the “nothing-to-hide” argument predicted by a model with pure instrumental preferences. I then propose three strategies that firms and researchers can adopt to improve data-driven decisions when shared data are influenced by consumers’ dual privacy concerns. First, by using an experiment to measure the joint distribution of privacy preferences, firms can extrapolate selection patterns to cases where the data utilization method changes. Second, when the joint privacy preference distribution is unknown, data collection should focus on representativeness over quantity, especially when information externality is present. Lastly, firms can learn about the selection pattern in the shared data by leveraging information contained in consumers’ data-sharing decisions.