A privacy-preserving method for lbs based on location semantics k-anonymity

Abstract

The invention provides an LBS (Location Based Service) privacy protecting method based on position semantics K-anonymity. The method comprises the steps of adopting a collaborative filtering method, and taking a space division grid as a unit to extract position semantic information included by a plurality of movement path data, i.e. obtaining position semantics through analyzing a time-space relationship between the space division grid and the historical data of the user movement; secondly, generating an anonymous data set which realizes simultaneously protecting position semantics privacy, position privacy, identification privacy and inquiry privacy based on the position semantics of the grid. The protecting method provided by the invention has the following advantages: the LBS privacy protecting method can be more conveniently integrated with a traditional time-space K-anonymous method by adopting the method of dividing the space grid to carry out position semantics statistics and expression; furthermore, four semantics, such as remaining, passby, crossing, no-passing and the like, can be generated by analyzing the historical data of the user movement tracks in an LBS analyzation, and the position semantics are more real.

Description

technical field

[0001] The invention relates to a location semantic K-anonymous LBS privacy protection method. Background technique

[0002] With the development and integration of technologies such as mobile communication technology, positioning technology, and geographic information, location-based services (Location-Based Service: LBS) have developed rapidly in recent years. It is characterized by pervasive scene perception and intelligent information processing, and has been rapidly applied in intelligent transportation, environmental monitoring, Internet of Things and other fields, and has brought huge economic benefits to these industries. However, with the in-depth development and wide application of LBS, privacy protection has also become a key issue to be solved in the further development of LBS. According to the characteristics of LBS pervasive scene perception and intelligent information processing, privacy in LBS is mainly divided into identity privacy (Who), lo...

Examples

Embodiment Construction

[0055] The invention will be described in further detail below in conjunction with the accompanying drawings.

[0056] First some basic definitions are given:

[0057] Definition 1 Movement trajectory: an ordered list of locations that record the user's continuous movement, defined as T PID =(1 ,t 1 >,2 ,t 2 >,...,n ,t n >),t 1 2 ...n , where PID represents the pseudonym identification of the trajectory, p i =(x,y),1≤i≤n,p i Indicates that the user is at t i The spatial position at the time, x, y represent the horizontal and vertical coordinate values ​​of the track point.

[0058] Definition 2 space division: For a 2-dimensional geometric space R that contains a moving track data set 2 ={p i |1≤i≤m},p i Indicates the spatial position of the trajectory point, and the corresponding 2-dimensional geometric space division is defined as: DR 2 ={Cell(col, row)|1≤col≤col_count, 1≤row≤row_count}, where each Cell(col, row) is called a space grid, and col, row represent the ...

technical field

[0001] The invention relates to a location semantic K-anonymous LBS privacy protection method. Background technique

[0002] With the development and integration of technologies such as mobile communication technology, positioning technology, and geographic information, location-based services (Location-Based Service: LBS) have developed rapidly in recent years. It is characterized by pervasive scene perception and intelligent information processing, and has been rapidly applied in intelligent transportation, environmental monitoring, Internet of Things and other fields, and has brought huge economic benefits to these industries. However, with the in-depth development and wide application of LBS, privacy protection has also become a key issue to be solved in the further development of LBS. According to the characteristics of LBS pervasive scene perception and intelligent information processing, privacy in LBS is mainly divided into identity privacy (Who), lo...

Examples

Embodiment Construction

[0055] The invention will be described in further detail below in conjunction with the accompanying drawings.

[0056] First some basic definitions are given:

[0057] Definition 1 Movement trajectory: an ordered list of locations that record the user's continuous movement, defined as T PID =(1 ,t 1 >,2 ,t 2 >,...,n ,t n >),t 1 2 ...n , where PID represents the pseudonym identification of the trajectory, p i =(x,y),1≤i≤n,p i Indicates that the user is at t i The spatial position at the time, x, y represent the horizontal and vertical coordinate values ​​of the track point.

[0058] Definition 2 space division: For a 2-dimensional geometric space R that contains a moving track data set 2 ={p i |1≤i≤m},p i Indicates the spatial position of the trajectory point, and the corresponding 2-dimensional geometric space division is defined as: DR 2 ={Cell(col, row)|1≤col≤col_count, 1≤row≤row_count}, where each Cell(col, row) is called a space grid, and col, row represent the ...

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