Online services such as web search, news portals, and ecommerce applications face the challenge of providing highquality experiences to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by personalizing services based on special knowledge about users. For example, a user's location, demographics, and search and browsing history may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information in return for enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can identify a near-optimal solution to the uti...