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Discovering hidden patterns from large amounts of data plays an important role in marketing, business, medical analysis, and other applications where these patterns are paramount for strategic decision making. However, recent research has shown that some discovered patterns can pose a threat to security and privacy. One alternative to address the privacy requirements in mining hidden patterns is to look for a balance between hiding restrictive patterns and disclosing non-restrictive ones. In this paper, we propose a new framework for enforcing privacy in mining frequent itemsets. We combine, in a single framework, techniques for efficiently hiding restrictive patterns: a transaction retrieval engine relying on an inverted file and Boolean queries; and a set of algorithms to" sanitize" a database. In addition, we introduce mining performance measures for frequent itemsets that quantify the fraction of mining patterns which are preserved after sanitizing a database. We also report the results of a performance evaluation of our research prototype and an analysis of the results.