6,562 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
Machine learning methods based on probabilistic decision tree under the multi-valued preference environment
In the classification calculation, the data are sometimes not
unique and there are different values and probabilities. Then, it is
meaningful to develop the appropriate methods to make classification decision. To solve this issue, this paper proposes the
machine learning methods based on a probabilistic decision tree
(DT) under the multi-valued preference environment and the
probabilistic multi-valued preference environment respectively for
the different classification aims. First, this paper develops a data
pre-processing method to deal with the weight and quantity
matching under the multi-valued preference environment. In this
method, we use the least common multiple and weight assignments to balance the probability of each preference. Then, based
on the training data, this paper introduces the entropy method
to further optimize the DT model under the multi-valued preference environment. After that, the corresponding calculation rules
and probability classifications are given. In addition, considering
the different numbers and probabilities of the preferences, this
paper also uses the entropy method to develop the DT model
under the probabilistic multi-valued preference environment.
Furthermore, the calculation rules and probability classifications
are similarly derived. At last, we demonstrate the feasibility of the
machine learning methods and the DT models under the above
two preference environments based on the illustrated examples
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