Journal of the ACM (JACM)
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Inference Control in Statistical Databases, From Theory to Practice
Disclosure Risk Assessment in Perturbative Microdata Protection
Inference Control in Statistical Databases, From Theory to Practice
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An epistemic framework for privacy protection in database linking
Data & Knowledge Engineering
Utility-based anonymization for privacy preservation with less information loss
ACM SIGKDD Explorations Newsletter
Privacy preserving decision tree learning over multiple parties
Data & Knowledge Engineering
Information disclosure under realistic assumptions: privacy versus optimality
Proceedings of the 14th ACM conference on Computer and communications security
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Guest editorial: Recent advances in preserving privacy when mining data
Data & Knowledge Engineering
The applicability of the perturbation based privacy preserving data mining for real-world data
Data & Knowledge Engineering
A Critique of k-Anonymity and Some of Its Enhancements
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
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
On the disclosure risk of multivariate microaggregation
Data & Knowledge Engineering
k-Anonymization with Minimal Loss of Information
IEEE Transactions on Knowledge and Data Engineering
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
Anonymizing healthcare data: a case study on the blood transfusion service
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate and large-scale privacy-preserving data mining using the election paradigm
Data & Knowledge Engineering
User-private information retrieval based on a peer-to-peer community
Data & Knowledge Engineering
Editorial: Recent progress in database privacy
Data & Knowledge Engineering
A survey of single-database private information retrieval: techniques and applications
PKC'07 Proceedings of the 10th international conference on Practice and theory in public-key cryptography
A three-dimensional conceptual framework for database privacy
SDM'07 Proceedings of the 4th VLDB conference on Secure data management
Priority-Based k-anonymity accomplished by weighted generalisation structures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Extending l-diversity to generalize sensitive data
Data & Knowledge Engineering
Information based data anonymization for classification utility
Data & Knowledge Engineering
| Hi-index | 0.00 |
Ideally, when microdata is anonymised, we should maximize both the level of privacy and the level information utility of a released microdata set. However, since privacy and information utility are conflicting requirements, it is difficult to find a good balance between the two goals. The objective and constraints of this optimization problem can be captured naturally with concepts from economic price theory. In this paper, we present an approach based on economic price theory for guiding the process of microdata anonymisation such that optimum levels of privacy and information utility are achieved.