Differential privacy protecting method of datastream key mode mining

A differential privacy and pattern mining technology, applied in the field of privacy protection, can solve problems such as privacy leakage, achieve the effect of improving utility and reducing computational complexity

Active Publication Date: 2018-05-04
GUANGXI NORMAL UNIV
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Problems solved by technology

[0008] What the present invention aims to solve is that the existing differential privacy protection method has the problem of privacy leakage when

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  • Differential privacy protecting method of datastream key mode mining
  • Differential privacy protecting method of datastream key mode mining
  • Differential privacy protecting method of datastream key mode mining

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Embodiment Construction

[0047] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific examples and with reference to the accompanying drawings.

[0048] There is a problem in using the method that satisfies differential privacy in frequent patterns on key patterns: the branch number condition is not considered, and the mined key patterns do not satisfy differential privacy. The present invention proposes a differential privacy protection method for data stream key pattern mining, such as figure 1 As shown, at each timestamp we design a 3-stage mechanism to return the key patterns mined at the current timestamp (i) as either a low-noise result set (ii) or an accurate approximate result set. This mechanism includes a preprocessing stage, a deep calculation stage and a mining stage. For window sliding, in order to increase the mining time and allocate the privacy budget...

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Abstract

The invention discloses a differential privacy protecting method of datastream key mode mining. A window corresponding to each timestamp is preprocessed and mined, firstly first noise adding is conducted, so that counting (a frequent mode) satisfies differential privacy, and then through a branch number distribution condition, all nodes are sifted to select a node candidate set C1 which satisfiesthe condition that c(primary set)>Sigmac(subsidiary set), and nodes in the C1 are subjected to secondary noise adding, so that the key mode satisfies the differential privacy. By conducting noise adding twice, on the premise that the safety is satisfied, the effectiveness can be improved. Besides, for window sliding, to improve reasonable distribution and privacy budget of the mining time, two differential nodes are utilized to calculate and decide noise adding situations, counting is considered, and the branch number condition is also considered, so that the mined key mode lowers the computing complexity as much as possible on the condition that the safety is guaranteed and is for study personnel to conduct analysis work.

Description

technical field [0001] The invention relates to the technical field of privacy protection, in particular to a differential privacy protection method for mining key patterns of data streams. Background technique [0002] With the advent of the information age, the information technology industry has entered a stage of rapid development. The Internet has penetrated into every field of human activities. The development of the Internet has produced a large amount of data in various fields such as politics, economy, culture, medical care and education, and mining these data can obtain a lot of useful information. Frequent Itemset Mining (FIM) is a core task in data mining and has been extensively studied. Key patterns as the best subset of frequent patterns have played a great role in many fields, such as decision support, Web usage mining, bioinformatics, and so on. For a stream of transactions, where each transaction contains a set of items, FIM tries to find sets of items t...

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Application Information

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IPC IPC(8): G06F21/62G06F17/30
CPCG06F21/6245G06F16/9027G06F16/90339
Inventor 王金艳刘陈傅星珵李先贤
Owner GUANGXI NORMAL UNIV
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