Combinatorial Algorithms: For Computers and Hard Calculators
Combinatorial Algorithms: For Computers and Hard Calculators
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Introduction to Clustering Large and High-Dimensional Data
Introduction to Clustering Large and High-Dimensional Data
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Comparing clusterings---an information based distance
Journal of Multivariate Analysis
Measuring unlinkability revisited
Proceedings of the 7th ACM workshop on Privacy in the electronic society
Measuring the Effectiveness and the Fairness of Relation Hiding Systems
APSCC '08 Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference
Using Linkability Information to Attack Mix-Based Anonymity Services
PETS '09 Proceedings of the 9th International Symposium on Privacy Enhancing Technologies
Modelling of Pseudonymity under Probabilistic Linkability Attacks
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
Attacking unlinkability: the importance of context
PET'07 Proceedings of the 7th international conference on Privacy enhancing technologies
A framework for quantification of linkability within a privacy-enhancing identity management system
ETRICS'06 Proceedings of the 2006 international conference on Emerging Trends in Information and Communication Security
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In this paper, we introduce a framework for measuring un-linkability both per subject and for an entire system. The framework enables the evaluator to attach different sensitivities to individual items in the system, and to specify the severity of different types of error that an adversary can make. These parameters, as well as a threshold that defines what constitutes a privacy breach, may be varied for each subject in the system; the framework respects and combines these potentially differing parametrisations. It also makes use of graphs in a way that results in intuitive feedback of different levels of detail.We exhibit the behaviour of our measures in two experimental settings, namely that of adversaries that output randomly chosen partitions, and that of adversaries that launch attacks of different effectiveness.