  In previous work, we presented a novel information-theoretic privacy
criterion for query forgery in the domain of information retrieval. Our
criterion measured privacy risk as a divergence between the user's and the
population's query distribution, and contemplated the entropy of the user's
distribution as a particular case. In this work, we make a twofold
contribution. First, we thoroughly interpret and justify the privacy metric
proposed in our previous work, elaborating on the intimate connection between
the celebrated method of entropy maximization and the use of entropies and
divergences as measures of privacy. Secondly, we attempt to bridge the gap
between the privacy and the information-theoretic communities by substantially
adapting some technicalities of our original work to reach a wider audience,
not intimately familiar with information theory and the method of types.
