A data distortion by probability distribution
ACM Transactions on Database Systems (TODS)
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Auditing Interval-Based Inference
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Capturing data usefulness and privacy protection in K-anonymisation
Proceedings of the 2007 ACM symposium on Applied computing
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Rethinking rank swapping to decrease disclosure risk
Data & Knowledge Engineering
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Privacy-MaxEnt: integrating background knowledge in privacy quantification
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Data utility and privacy protection trade-off in k-anonymisation
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Injector: Mining Background Knowledge for Data Anonymization
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Towards Preference-Constrained k-Anonymisation
Database Systems for Advanced Applications
Preventing interval-based inference by random data perturbation
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
Utility-preserving transaction data anonymization with low information loss
Expert Systems with Applications: An International Journal
K-anonymous path privacy on social graphs
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
| Hi-index | 12.07 |
k-Anonymisation is an approach to preventing sensitive information about individuals being identified or inferred from a dataset. Existing work achieves this by ensuring that each individual is linked to multiple sensitive values, but they have not adequately considered how the range formed by these sensitive values may affect privacy protection. When such a range is small, sensitive information about individuals may still be inferred quite accurately, thereby breaching privacy. In this paper, we study the problem of range disclosure (i.e. estimating sensitive information through ranges) in k-anonymisation, and propose Range Diversity for quantifying the effect of range disclosure on privacy protection. Our measure considers several possible attacks and allows anonymisers to specify the level of protection required in a flexible manner. Extensive experiments show that range diversity provides better protection for range disclosure and higher level of data utility than the existing methods.