Information Loss Evaluation based on Fuzzy and Crisp Clustering of Graph Statistics
Publication Type:
Conference PaperSource:
IEEE World Congress on Computational Intelligence (WCCI) 2012, Brisbane, Australia (2012)Keywords:
data privacy; fuzzy clustering; graphsAbstract:
In this paper we apply different types of clustering,
fuzzy (fuzzy c-Means) and crisp (k-Means) to graph statistical
data in order to evaluate information loss due to perturbation as
part of the anonymization process for a data privacy application.
We make special emphasis on two major node types: hubs, which
are nodes with a high relative degree value, and bridges, which
act as connecting nodes between different regions in the graph.
By clustering the graph's statistical data before and after
perturbation, we can measure the change in characteristics and
therefore the information loss. We partition the nodes into three
groups: hubs/global bridges, local bridges, and all other nodes.
We suspect that the partitions of these nodes are best represented
in the fuzzy form, especially in the case of nodes in frontier
regions of the graphs which may have an ambiguous assignment.
Fuzzy methods for database protection
Publication Type:
Conference ProceedingsSource:
7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011) and LFA-2011, Atlantis Press, Volume 1-1, Aix-les-Bains, France, p.439 - 443 (2011)ISBN:
978-90-78677-00-0URL:
http://www.atlantis-press.com/php/paper-details.php?from=author+index&id=2328&querystr=Keywords:
Data privacy; fuzzy clustering; fuzzy measures; fuzzy integralsAbstract:
Data privacy has become an important topic of research. Ubiquitous databases and the eclosion of web technology eases the access to information. This information can be related to individuals, and, thus, sensitive information about users can be easily accessed by interested parties. Data privacy focuses on tools and methods to protect the privacy of the respondents and data owners. In the last years, a large number of methods have been developed for data privacy. Some of them are based on fuzzy sets and systems. In this position paper we present a review of some of our results in this area. In particular, we focus on the use of fuzzy sets for data protection, for measuring information loss and for measuring disclosure risk. The techniques used in this field and reviewed in this paper range from fuzzy clustering to fuzzy integrals.
