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
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Screening and interpreting multi-item associations based on log-linear modeling
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Framework for High-Accuracy Privacy-Preserving Mining
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Privacy Preserving Categorical Data Analysis with Unknown Distortion Parameters
Transactions on Data Privacy
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Randomized Response techniques have been empirically investigated in privacy preserving association rule mining. In this paper, we investigate the accuracy (in terms of bias and variance of estimates) of both support and confidence estimates of association rules derived from the randomized data. We demonstrate that providing confidence on data mining results from randomized data is significant to data miners. We propose the novel idea of using interquantile range to bound those estimates derived from the randomized market basket data. The performance is evaluated using both representative real and synthetic data sets.