Track data zero-watermarking method using spatio-temporal characteristics

Abstract

The invention discloses a trajectory data zero-watermarking method using spatio-temporal characteristics. The method comprises the following steps: firstly, carrying out feature point extraction and stay area division on track data by adopting a method for segmenting sub-tracks in a stay area (SESA), and dividing the track into a plurality of sub-tracks by taking the stay area as a node; and then, constructing a watermark index through the moving speed of each sub-track in the X direction, and generating watermark information by adopting a voting principle. And finally, carrying out XOR operation on the generated watermark information and the encrypted copyright image to generate a zero watermark. The method is suitable for high-precision trajectory data, common data processing operation and hostile attacks of the trajectory data can be resisted, and a new solution is provided for security application and copyright protection of the trajectory data.

Description

Technical Field

[0001] The invention belongs to the technical field of geographic information security and relates to a zero-watermark method for trajectory data using spatiotemporal features. Background Art

[0002] Trajectory data is key data that records the movement trajectory of an object in space and time. It has the characteristics of large scale, real-time dynamics, and diverse types. It is widely used in smart city construction, traffic optimization, location services and other fields. It contains rich geographic information and can also reveal deep information such as users' activity patterns and travel patterns. It has huge commercial and social value. If there is a lack of effective copyright protection, the abuse of data may pose a serious threat to personal privacy, enterprises and even national security. Therefore, ensuring the integrity, security and source traceability of trajectory data has become an urgent problem to be solved.

[0003] As an important technology in the field of information security, digital watermarking can be used for copyright protection of trajectory data. For copyright protection of trajectory datasets, Claudio Lucchese et al. embedded watermarks by modifying the amplitude coefficients of the discrete Fourier transform of trajectory point coordinates in order to improve the ability to resist malicious attacks. Ren et al. proposed a zero watermark method for trajectory data that can resist random point deletion attacks and other common attacks. The watermark information is constructed by the invariant moments generated by the trajectory data, which has better stability against geometric attacks and deletion attacks, but the watermark method is not robust enough against other attacks. Although some research has been done on copyright protection methods for trajectory data, it is still in its infancy. The current trajectory data watermarking scheme has the following shortcomings: copyright information is mostly hidden in the data through embedded watermarking methods, while trajectory data is mostly stored in text form, and the watermark has poor robustness; the existing watermarks do not consider the characteristics of the trajectory data itself, and the watermarking scheme is not targeted. Summary of the invention

[0004] The purpose of the present invention is to provide a zero-watermark method for trajectory data using spatiotemporal features, aiming to provide an effective copyright protection solution for trajectory data.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for generating a zero watermark of trajectory data, comprising:

[0007] Read the coordinates of the trajectory data, convert the data into a coordinate system, convert the geographic coordinate system into a projection coordinate system, use the stop area segmentation sub-trajectory method SESA to extract the stop area and feature points from the trajectory T, and divide the trajectory into sub-trajectories with the first and last stop points of each stop area as nodes;

[0008] Calculate the moving speed in the X and Y directions of each sub-track, and record the maximum and minimum values ​​of the speed in the X direction as max_x, min_x;

[0009] The index value index is calculated according to the moving speed speed_x in the X direction of each sub-track through the following formula, where int() is the rounding function, k is the magnification factor of the normalized coordinate value, and L is the size of the copyright image; index=int(((speed_x-min_x) / (max_x-min_x))×10 k )mod L

[0010] Define an integer sequence W′={w′ i =0, i=1,2,…,N w},N w is the length of the integer sequence, which is the same as the size of the copyright image. The feature matrix is ​​constructed according to the following formula, where speed_x and speed_y are the moving speeds in the X and Y directions of each sub-track;

[0011] Binarize the integer sequence W′ and convert it into a feature matrix according to the following formula;

[0012] The copyright image is encrypted by Logistic chaotic mapping and converted into a one-dimensional sequence, denoted as W;

[0013] Perform an XOR operation on the one-dimensional feature matrix W′ and the encrypted copyright image W. According to the size of the original copyright image, the one-dimensional sequence after the XOR operation is reconstructed into a two-dimensional zero-watermark image W * ;

[0014] A zero watermark detection method for trajectory data, comprising:

[0015] Read the trajectory data to be detected, repeat the construction process of the feature matrix in the zero watermark generation method, and construct the feature matrix, which is recorded as M;

[0016] Combine M with the zero-watermark image W * Perform XOR operation to obtain the encrypted copyright image;

[0017] The encrypted copyright image is decrypted using Logistic chaotic mapping to obtain the original copyright image. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the implementation examples of the present invention or the technical solutions in the prior art, the drawings required for use in the implementation examples or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only schematic diagrams of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0019] Figure 1 A flow chart of a method for generating zero watermark of trajectory data provided by an embodiment of the present invention;

[0020] Figure 2 A flow chart of a zero watermark detection method for trajectory data provided by an embodiment of the present invention;

[0021] Figure 3 Example data of trajectory data provided for implementation examples of the present invention;

[0022] Figure 4 After the trajectory data example data provided for the implementation example of the present invention is divided into four sub-data, four zero-watermark images are generated for different sub-data. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the implementation examples of the present invention to clearly and completely describe the technical solutions in the implementation examples of the present invention. Obviously, the described implementation examples are only part of the implementation examples of the present invention, not all of the implementation examples. Based on the implementation examples in the present invention, all other implementation examples obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] The following is a detailed description of a zero watermark method for trajectory data using spatiotemporal features of the present invention in conjunction with the accompanying drawings and implementation examples;

[0025] Zero watermark example:

[0026] Please refer to the attached manual Figure 3 Taking the data in as an example, the trajectory data coordinates are read, the data is converted into a coordinate system, the geographic coordinate system is converted into a projection coordinate system, and the stop area segmentation sub-trajectory method SESA is used to extract the stop area and feature points from the trajectory T, and the first and last stop points of each stop area are used as nodes to divide the trajectory into sub-trajectories;

[0027] Calculate the moving speed in the X and Y directions of each sub-track, and record the maximum and minimum values ​​of the speed in the X direction as max_x, min_x;

[0028] The index value index is calculated according to the moving speed speed_x in the X direction of each sub-track through formula (1), where int() is the rounding function, k is the magnification factor of the normalized coordinate value, and L is the size of the copyright image; index=int(((speed_x-min_x) / (max_x-min_x))×10 k )mod L (1)

[0029] Define an integer sequence W′={w′ i =0, i=1,2,…,N w},N w is the length of the integer sequence, which is the same as the size of the copyright image. The feature matrix is ​​constructed according to formula (2), where speed_x and speed_y are the moving speeds in the X and Y directions of each sub-trajectory;

[0030] According to formula (3), the integer sequence W′ is binarized and converted into a feature matrix;

[0031] The copyright image is encrypted by Logistic chaotic mapping and converted into a one-dimensional sequence with a size of L, denoted by W;

[0032] The one-dimensional feature matrix W′ is XORed with the encrypted copyright image W. According to the size of the original copyright image, the one-dimensional sequence after the XOR operation is reconstructed into a two-dimensional zero watermark image W* to generate a zero watermark as shown in the accompanying figure of the explanatory data. Figure 4 As shown;

[0033] Zero watermark detection example:

[0034] Read the trajectory data to be detected, repeat the construction process of the feature matrix in the zero watermark generation example, and construct the feature matrix, which is recorded as M;

[0035] Combine M with the zero-watermark image W * Perform XOR operation to obtain the encrypted copyright image;

[0036] Perform Logistic chaotic mapping decryption on the encrypted copyright image to obtain the original copyright image;

[0037] The above description of the disclosed examples enables professionals in the field to implement or use the present invention. Various modifications to these embodiments will be apparent to professionals in the field, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest range consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating zero watermark of trajectory data, characterized in that: include: Read the coordinates of the trajectory data, convert the data into a coordinate system, convert the geographic coordinate system into a projection coordinate system, use the stop area segmentation sub-trajectory method SESA to extract the stop area and feature points from the trajectory T, and divide the trajectory into sub-trajectories with the first and last stop points of each stop area as nodes; Calculate the moving speed in the X and Y directions of each sub-track, and record the maximum and minimum values ​​of the speed in the X direction as max_x, min_x; The index value index is calculated according to the moving speed speed_x in the X direction of each sub-track through the following formula, where int() is the rounding function, k is the magnification factor of the normalized coordinate value, and L is the size of the copyright image; index=int(((speed_x-min_x) / (max_x-min_x))×10 k )mod L Define an integer sequence w′={w i ′=0,i=1,2,…,N w },N w is the length of the integer sequence, which is the same as the size of the copyright image. The feature matrix is ​​constructed according to the following formula, where speed_x and speed_y are the moving speeds in the X and Y directions of each sub-track; Binarize the feature matrix W′ according to the following formula; The copyright image is encrypted by Logistic chaotic mapping and converted into a one-dimensional sequence with a size of L, denoted by W; Perform an XOR operation on the one-dimensional feature matrix W′ and the encrypted copyright image W, and reconstruct the one-dimensional sequence after the XOR operation into a two-dimensional zero-watermark image W * .

2. A zero watermark detection method for trajectory data, characterized in that: include: According to the method for generating zero watermark of trajectory data according to claim 1, a feature matrix is ​​generated, which is denoted as M; Combine M with the zero-watermark image W * Perform XOR operation to obtain the encrypted copyright image; The encrypted copyright image is decrypted using Logistic chaotic mapping to obtain the original copyright image.

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