2011/11/17 by David Rebollo-Monedero, David Rebollo‐Monedero, Javier Parra‐Arnau +5 · 1 citation
Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information Theory (cs.IT) #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #cs.CR #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1111.4045
This paper has 15 pages and 1 figure
arxiv created 2011/11/17 · openalex publication_date 2011/11/17 · arxiv updated 2015/03/19 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
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.