A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from tredning data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a-novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.

We present a framework for mining association rules from transactions consisting of categorical items where the data has been randomized to preserve privacy of individual transactions. While it is feasible to recover association rules and preserve privacy using a straightforward "uniform" randomization, the discovered rules can unfortunately be exploited to find privacy breaches. We analyze the nature of privacy breaches and propose a class of randomization operators that are much more effective than uniform randomization in limiting the breaches. We derive formulae for an unbiased support estimator and its variance, which allow us to recover itemset supports from randomized datasets, and show how to incorporate these formulae into mining algorithms. Finally, we present experimental results that validate the algorithm by applying it on real datasets.

Privacy preserving data mining is a novel research direction in data mining and statistical databases, where data mining algorithms are analyzed for the side-effects they incur in data privacy. There have been many studies on efficient discovery of frequent itemsets in privacy preserving data mining. However, it is nontrivial to maintain such discovered frequent itemsets because a database may allow frequent itemsets updates and such frequent itemsets may be turned into infrequent itemsets. In this paper, an incremental updating algorithm IPPFIM is proposed for efficient maintenance of discovered frequent itemsets when new transaction data are added to a transaction database in privacy preserving. The algorithm makes use of previous mining results to cut down the cost of finding new frequent itemsets in an updated database, the performance evaluation shows the efficiency of this method. Keyword: Data mining, privacy-preserving, incremental