k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
(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
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies
IEEE Transactions on Knowledge and Data Engineering
A family of enhanced (L,α)-diversity models for privacy preserving data publishing
Future Generation Computer Systems
Relationships and data sanitization: a study in scarlet
Proceedings of the 2010 workshop on New security paradigms
Extended k-anonymity models against sensitive attribute disclosure
Computer Communications
Implicit: a multi-agent recommendation system for web search
Autonomous Agents and Multi-Agent Systems
Satisfying privacy requirements: one step before anonymization
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
On the identity anonymization of high-dimensional rating data
Concurrency and Computation: Practice & Experience
| Hi-index | 0.00 |
Most existing works of data anonymisation target at the optimization of the anonymisation metrics to balance the data utility and privacy, whereas they ignore the effects of a requester's trust level and application purposes during the data anonymisation. Our aim of this paper is to propose a much finer level anonymisation scheme with regard to the data requester's trust value and specific application purpose. We prioritize the attributes for anonymisation based on how important and critical they are related to the specified application purposes and propose a trust evaluation strategy to quantify the data requester's reliability, and further build the projection between the trust value and the degree of data anonymiztion, which intends to determine to what extent the data should be anonymized. The decomposition algorithm is developed to find the desired anonymous solution, which guarantees the uniqueness and correctness.