10,148 research outputs found
On the Measurement of Privacy as an Attacker's Estimation Error
A wide variety of privacy metrics have been proposed in the literature to
evaluate the level of protection offered by privacy enhancing-technologies.
Most of these metrics are specific to concrete systems and adversarial models,
and are difficult to generalize or translate to other contexts. Furthermore, a
better understanding of the relationships between the different privacy metrics
is needed to enable more grounded and systematic approach to measuring privacy,
as well as to assist systems designers in selecting the most appropriate metric
for a given application.
In this work we propose a theoretical framework for privacy-preserving
systems, endowed with a general definition of privacy in terms of the
estimation error incurred by an attacker who aims to disclose the private
information that the system is designed to conceal. We show that our framework
permits interpreting and comparing a number of well-known metrics under a
common perspective. The arguments behind these interpretations are based on
fundamental results related to the theories of information, probability and
Bayes decision.Comment: This paper has 18 pages and 17 figure
Ontology-Based Quality Evaluation of Value Generalization Hierarchies for Data Anonymization
In privacy-preserving data publishing, approaches using Value Generalization
Hierarchies (VGHs) form an important class of anonymization algorithms. VGHs
play a key role in the utility of published datasets as they dictate how the
anonymization of the data occurs. For categorical attributes, it is imperative
to preserve the semantics of the original data in order to achieve a higher
utility. Despite this, semantics have not being formally considered in the
specification of VGHs. Moreover, there are no methods that allow the users to
assess the quality of their VGH. In this paper, we propose a measurement
scheme, based on ontologies, to quantitatively evaluate the quality of VGHs, in
terms of semantic consistency and taxonomic organization, with the aim of
producing higher-quality anonymizations. We demonstrate, through a case study,
how our evaluation scheme can be used to compare the quality of multiple VGHs
and can help to identify faulty VGHs.Comment: 18 pages, 7 figures, presented in the Privacy in Statistical
Databases Conference 2014 (Ibiza, Spain
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