Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
The VLDB Journal — The International Journal on Very Large Data Bases
A unified framework for protecting sensitive association rules in business collaboration
International Journal of Business Intelligence and Data Mining
A privacy preserving technique for distance-based classification with worst case privacy guarantees
Data & Knowledge Engineering
Preserving Privacy in Time Series Data Classification by Discretization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A distributed approach to enabling privacy-preserving model-based classifier training
Knowledge and Information Systems
A new class of attacks on time series data mining\m{1}
Intelligent Data Analysis
Performance measurements for privacy preserving data mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Privacy-preserving subgraph discovery
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
Preserving Privacy in Time Series Data Mining
International Journal of Data Warehousing and Mining
Distributed and Parallel Databases
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Randomization is an economical and efficient approach for privacy preserving data mining (PPDM). In order to guarantee the performance of data mining and the protection of individual privacy, optimal randomization schemes need to be employed. This paper demonstrates the construction of optimal randomization schemes for privacy preserving density estimation. We propose a general framework for randomization using mixture models. The impact of randomization on data mining is quantified by performance degradation and mutual information loss, while privacy and privacy loss are quantified by interval-based metrics. Two different types of problems are defined to identify optimal randomization for PPDM. Illustrative examples and simulation results are reported.