An Information-Theoretic Privacy Criterion for Query Forgery in Information Retrieval

TL;DR

This paper refines an information-theoretic privacy criterion for query forgery in information retrieval, clarifying its theoretical basis and making it more accessible to a broader audience.

Contribution

It provides a detailed interpretation of the privacy metric and adapts technical aspects to bridge the gap between privacy and information theory communities.

Findings

Clarified the connection between entropy maximization and privacy measures

Justified the use of divergence as a privacy metric

Made the methodology more accessible to non-specialists

Abstract

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.