Privacy preserving frequent itemset mining

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State-of-the-art in privacy preserving data mining (2004, background): “The work in [ 19 ] builds on top of the work previously presented, and aims at balancing between privacy and disclosure of information by trying to minimize the impact on sanitized transactions or else to minimize the accidentally hidden and ghost rules.”
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, methodology): “For example, the perturbation-based association rule hiding technique [ 49 ], [50] is implemented by changing a selected set of 1-values to 0-values or vice versa so that the frequent item sets that generate the rule are hidden or the support of sensitive rules is lowered to a user-specified thresho”
Privacy Preserving Data Mining Research: Current Status and Key Issues (2007, background): “Efficiency , which concerns the ability of an algorithm to execute with good performance in terms of all the resources implied by the algorithm; Performance is assessed, as usually, in terms of computational costs [14] [31].”
A Framework for Evaluating Privacy Preserving Data Mining Algorithms* (2005, background): “From the organizations point of view, Privacy involves the de(cid:30)nition of policies stating which information is collected, how it is used, and how customers are informed and involved in this process.”