Activity time sequence track mining method based on local differential privacy

A differential privacy and time series technology, applied in geographic information databases, digital data protection, computer security devices, etc., can solve the problem of not being able to obtain information about the client's activity time series trajectory, and achieve the effect of improving accuracy and reducing sample space.

Active Publication Date: 2019-12-13
HARBIN INST OF TECH AT WEIHAI
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0009] The present invention is to solve the problem that the existing local differential privacy model can only obtain the occurrence frequency information of the collected client event elements, but cannot obtain the situation information of the client's activity sequence trajectory, and provides an activity sequence based on local differential privacy The trajectory mining method makes it possible to infer the user's activity timing trajectory while meeting the requirements of the local differential privacy framework

Method used

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  • Activity time sequence track mining method based on local differential privacy
  • Activity time sequence track mining method based on local differential privacy
  • Activity time sequence track mining method based on local differential privacy

Examples

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Embodiment Construction

[0029] Below by embodiment, further illustrate the present invention.

[0030] as follows figure 1 As shown, this embodiment is described with the method of analyzing the trajectory of the regional flow of people, and the steps are as follows:

[0031] Step 1: Obtain map data. The original data used in this example is the positioning data collected from 182 volunteers in a certain city within five years (2007-2012).

[0032] Step 2: Divide the map into m disjoint areas, and call the adjacent areas domains, and regard the area where the client is located at a certain moment as an event element, then an activity timing track is a track, usually In other words, the client trajectory is continuous. Specifically, select the volunteer positioning data from 6:00 to 9:00 in the morning and the area with relatively dense activities (39.8-40.1, 116.2-116.4), and divide the area into 10,000 unit areas with 0.003 and 0.002 units respectively , a total of 124292 experimental data, Fig...

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Abstract

The invention relates to the field of data privacy protection. The activity time sequence track mining method based on local differential privacy is mainly used for researching how to ensure to meet the requirement of local differential privacy and ensure to mine the activity time sequence track from data in the scene of collection and mining of personal data. A system is provided with a client for acquiring data and a local client. The method comprises: the client for collecting data adding noise to original private data adopting a client algorithm locally, enabling the original privacy datato meet a local differential privacy requirement of a privacy protection budget parameter; storing he original privacy data in a local client, randomly selecting a pair of records with a sequential relationship from a data record set by the local client, then converting the records into a sequential matrix, and then performing noise adding processing. The method can be widely applied to mining ofactivity time sequence tracks based on local differential privacy.

Description

technical field [0001] The present invention relates to the field of data privacy protection, and mainly studies how to ensure that the data can meet the requirements of local differential privacy in the scenario of personal data collection and mining, and can also ensure that the activity sequence trajectory can be mined from the data, especially involving a A method for mining activity temporal traces based on local differential privacy. [0002] technical background [0003] With the advent of the era of big data, data has become a valuable resource. This is mainly due to the emergence of various data mining methods, which can dig out more potential information from the data, and also contain many users' personal privacy, and the mined information can be divided into the following three types: [0004] 1. Traditional differential privacy: It is a new privacy protection framework proposed by Dwork for the privacy leakage of statistical databases. It is the first strict dat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06F16/29G06F21/62
CPCG06F21/6245G06F16/2465G06F16/29
Inventor 张兆心闫健恩许海燕王雁王帅
Owner HARBIN INST OF TECH AT WEIHAI
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