  Online offerings such as web search, news portals, and e-commerce
applications face the challenge of providing high-quality service to a large,
heterogeneous user base. Recent efforts have highlighted the potential to
improve performance by introducing methods to personalize services based on
special knowledge about users and their context. For example, a users
demographics, location, and past search and browsing 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 by services to such information. We
introduce and explore an economics of privacy in personalization, where people
can opt to share personal information, in a standing or on-demand manner, in
return for expected 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 find a provably
near-optimal optimization of the utility-privacy tradeoff in an efficient
manner. We evaluate our methodology on data drawn from a log of the search
activity of volunteer participants. We separately assess users' preferences
about privacy and utility via a large-scale survey, aimed at eliciting
preferences about peoples' willingness to trade the sharing of personal data in
returns for gains in search efficiency. We show that a significant level of
personalization can be achieved using a relatively small amount of information
about users.
