信息检索中查询伪造的信息论隐私准则
An Information-Theoretic Privacy Criterion for Query Forgery in Information Retrieval
摘要：
在以前的工作中，我们提出了一个新的信息理论隐私准则查询伪造领域的信息检索。我们的标准衡量隐私风险之间的分歧用户和人口的查询分布，并考虑作为一个特定的情况下，用户的分布熵。在这项工作中，我们做出了双重贡献。首先，我们彻底解释和证明我们以前的工作中提出的隐私度量，阐述了著名的熵最大化方法和使用熵和分歧作为隐私的措施之间的密切联系。其次，我们试图弥合隐私和信息理论社区之间的差距，通过大量调整我们原来的工作，以达到更广泛的受众，不熟悉信息论和类型的方法的一些技术。
英文摘要：
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.
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