A frequent item set mining method for differential privacy protection

Abstract

The invention discloses a frequent item set mining method for differential privacy protection, which comprises the following steps of: calculating support degrees of all item sets, and selecting frequent item sets from the support degrees; counting the length of each transaction in the data set, calculating a truncation length L and truncating the data set; calculating the upper limit m of the number of items contained in the frequent item set and the number of frequent items, and constructing a set F consisting of the frequent items according to the value; constructing a maximum frequent itemset MFI set B and a candidate item set C; adding noise to the item set in the set C by using the set B; calculating the support degree of each candidate item set by using the initial MFI set B, and then calculating the sum E of errors with the real support degree; Searching B, B in B, replacing B with B, and updating values of the error and E; and stopping iteration and outputting a result when the sum of the errors is no longer reduced. According to the method, personal privacy leakage caused by frequent item set release can be well prevented, and meanwhile, the availability of a mining result is effectively improved through data set truncation operation.

Description

technical field [0001] The invention relates to a frequent item set mining method for differential privacy protection, which belongs to the technical field of information security. Background technique [0002] With the rapid development of cloud computing and big data, data mining technology has made great progress in some in-depth research and applications. Frequent itemset mining is one of the core problems of data mining. Its goal is to find frequent itemsets in the data set, and it is widely used in real life. Although it can provide valuable information, publishing frequent itemsets without processing may cause serious personal privacy leakage. How to improve the availability of frequently published itemsets while protecting personal privacy has become one of the urgent problems to be solved in the field of data mining. With the proposal and development of privacy protection technology, differential privacy protection method has become a popular privacy protection te...

AI Technical Summary

Problems solved by technology

Differential privacy is realized through the noise mechanism, that is, random noise is added to the output to protect data security. The greater the noise added, the more secure the data, however, the lower the availability of the data, and vice versa

[0003] At present, there are many frequent itemset mining algorithms that satisfy differential privacy (such as PrivBasis algorithm, PrivSuper algorithm, etc.), but these algorithms are only suitable for processing low-dimensional data sets, and they will be too sensitive when processing high-dimensional data sets. Add more noise to the problem, resulting in low usability of mining results

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