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Many enterprise websites provide search engines to facilitate customer access to their underlying documents or data. With the web interface of such a search engine, a customer can specify one or a few keywords that he/she is interested in; and the search engine returns a list of documents/tuples matching the user-specified keywords, sorted by an often-proprietary scoring function. It was traditionally believed that, because of its highly-restrictive interface (i.e., keyword search only, no SQL-style queries), such a search engine serves its purpose of answering individual keyword-search queries without disclosing big-picture aggregates over the data which, as we shall show in the paper, may incur significant privacy concerns to the enterprise. Nonetheless, recent work on sampling and aggregate estimation over a search engine's corpus through its keyword-search interface transcends this traditional belief. In this paper, we consider a novel problem of suppressing sensitive aggregates for enterprise search engines while maintaining the quality of answers provided to individual keyword-search queries. We demonstrate the effectiveness and efficiency of our novel techniques through theoretical analysis and extensive experimental studies.