StreamKrimp: Detecting Change in Data Streams
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Mining Databases to Mine Queries Faster
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
ACM SIGKDD Explorations Newsletter
Summarising data by clustering items
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
Summarizing data succinctly with the most informative itemsets
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
Summarizing categorical data by clustering attributes
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Many databases will not or can not be disclosed without strong guarantees that no sensitive information can be extracted. To address this concern several data perturbation techniques have been proposed. However, it has been shown that either sensitive information can still be extracted from the perturbed data with little prior knowledge, or that many patterns are lost. In this paper we show that generating new data is an inherently safer alternative. We present a data generator based on the models obtained by the MDLbased KRIMP [12] algorithm. These are accurate representations of the data distributions and can thus be used to generate data with the same characteristics as the original data. Experimental results show a very large patternsimilarity between the generated and the original data, ensuring that viable conclusions can be drawn from the anonymised data. Furthermore, anonymity is guaranteed for suited databases and the quality privacy trade-off can be balanced explicitly.