Frequent item set mining method and system supporting differential privacy

Abstract

The invention discloses a frequent item set mining method and system supporting differential privacy, and belongs to the field of computer data privacy protection and data mining. The method comprises the steps of processing an original data set by adopting a transaction truncation method, and avoiding privacy leakage in the truncation process by using an exponential difference mechanism-based method; then constructing a tree structure, wherein each node corresponds to an item of a transaction, frequent information on the transaction is stored in a node corresponding to the last item of the transaction, and the support degree of each node is initialized by using Laplace noise to avoid the privacy leakage in the tree construction process; and converting the constructed tree into an FP tree, so that a frequent item set can be mined by using an FP-Growth method. According to the method and the system, the released frequent item set can meet security demands of the differential privacy; compared with an original frequent item set, the released frequent item set has relatively high similarity with the original frequent item set and relatively high data availability; and compared with a conventional algorithm, higher efficiency is achieved.

Description

technical field [0001] The invention belongs to the technical field of computer data privacy protection and data mining, and more specifically relates to a frequent item set mining method and system supporting differential privacy. Background technique [0002] With the advent of the era of big data, more and more data are being collected and analyzed by some statistical agencies. Sometimes, these agencies will release some data for use by third parties, and these data may contain some sensitive information of the data collected, such as a patient suffering from a certain disease. Therefore, publishing these data directly will cause the privacy of the data collectors to leak. In order to protect the privacy of the data collectors, the data issuer must take privacy protection measures. [0003] At present, privacy protection is widely used in frequent itemsets mining. The existing frequent itemsets mining method that supports privacy protection is mainly the PrivBasis metho...

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Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a frequent itemset mining method and system supporting differential privacy. The inefficiency of the method and the technical problem of wasting resources due to the generation of a large number of useless θ-basis

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