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
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
PRBAC: An Extended Role Based Access Control for Privacy Preserving Data Mining
Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science
Privacy preserving Data Mining Algorithms without the use of Secure Computation or Perturbation
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
A decision tree-based missing value imputation technique for data pre-processing
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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Data preprocessing plays an important role in data mining for ensuring better quality of data. The data being extracted from raw data will contain impurities, noisy data which leads to inefficient data analysis, inaccurate decisions and user inconveniencies. Data pre-processing tasks involves identifying the outliers, cleaning of noisy data. In this paper we present a "Decision tree based Missing value Imputation technique" (DMI) which makes use of an EM algorithm and a decision tree (DT) algorithm. The result of pre-processed data should be secured using the privacy preserving techniques. The privacy of the data can be obtain by applying the cryptographic techniques which provides access to the stored data based on individual's roles that makes the data secure from the unauthorised access.