On Privacy and Accuracy in Data Releases

Resumo

In this paper we study the relationship between privacy and accuracy in the context of correlated datasets. We use a model of quantitative information flow to describe the the trade-off between privacy of individuals’ data and and the utility of queries to that data by modelling the effectiveness of adversaries attempting to make inferences after a data release. We show that, where correlations exist in datasets, it is not possible to implement optimal noise-adding mechanisms that give the best possible accuracy or the best possible privacy in all situations. Finally we illustrate the trade-off between accuracy and privacy for local and oblivious differentially private mechanisms in terms of inference attacks on medium-scale datasets.

Publicação
31st International Conference on Concurrency Theory
Gabriel H. Nunes
Gabriel H. Nunes
Doutorando em Ciência da Computação

Físico e Cientista da Computação

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