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A method for mining activity time-series traces based on local differential privacy

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

Active Publication Date: 2021-07-27
HARBIN INST OF TECH AT WEIHAI
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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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  • A method for mining activity time-series traces based on local differential privacy
  • A method for mining activity time-series traces based on local differential privacy
  • A method for mining activity time-series traces based on local differential privacy

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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 in units of 0.003 and 0.002 respectively , a total of 124,292 experimental data. Fo...

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Abstract

The present invention relates to the field of data privacy protection, and mainly studies how to ensure that the data meets the requirements of local differential privacy in the context of personal data collection and mining, and can also ensure that the time-series trajectory of activities can be mined from the data. The method of mining activity time-series trajectory of differential privacy has a client and a local client for collecting data, and the client for collecting data locally adds noise to the original private data using the client algorithm, so that it meets the privacy protection budget parameter as According to local differential privacy requirements, the original privacy data will be stored in the local client, and the local client will randomly select a pair of records with a sequential relationship from the data record set, and then convert them into a time series matrix, followed by noise processing. The present invention can be widely applied to the mining of time-series traces of activities 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 Patents(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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