The anonymization of widely distributed or open data has been a topic of great interest to privacy advocates in recent years. The goal of anonymization in these cases is to make data available to a larger audience, extending the utility of the data to new environments and evolving use cases without compromising the personal information of individuals whose data are being distributed. The resounding issue with such practices is that, with any anonymity measure, there is a trade-off between privacy and utility, where maximizing one carries a cost to the other. In this paper, the authors propose a framework for the utility-preserving release of anonymized data, based on the idea of intelligent data brokerages. These brokerages act as intermediaries between users requesting access to information resources and an existing database management system (DBMS). Through the use of a formal language for interpreting user information requests, customizable anonymization policies, and optional natural language processing (NLP) capabilities, data brokerages can maximize the utility of data in-context when responding to user inquiries.