Ellipsoid space mapping-based vector geographic data controllable de-encryption method and system

Abstract

The application aims to provide a vector geographic data controllable de-encryption method and system based on ellipsoid space mapping. Since the ellipsoid surface of the earth ellipsoid has the characteristics of nonlinearity and smooth change, if the vector geographic data is established based on the ellipsoid surface to carry out space mapping, not only the original coordinate information cannot be restored through inverse transformation, but also the coordinates can still keep smooth change after the space mapping of the model, so as to meet the security and usability requirements of the de-encrypted data. In addition, by setting different de-encryption precisions to adjust the vector geographic data of the corresponding scene, the controllable de-encryption requirement can be met.

Description

Technical Field

[0001] This invention belongs to the field of geographic information security protection, specifically involving a method for declassifying vector geographic data. Background Technology

[0002] Vector geographic data is characterized by its large volume, high positioning accuracy, and diverse types of classified information. Its leakage would pose a serious threat to national security and interests. To ensure the legal and compliant use of geographic information data, in accordance with the "Surveying and Mapping Law of the People's Republic of China" and the "Notice on the Research and Development and Service of Geographic Information Confidentiality Processing Technology" (Notice No. 22), it is necessary to declassify vector geographic data to meet the requirements for secure sharing and use.

[0003] Currently, the main method used is geometric location precision declassification to reduce the location precision of vector geographic data, transforming it into publicly usable data, thereby achieving the goals of protecting vector geographic data and enabling secure data sharing. Vector geographic data declassification must meet four requirements: First, security requirements. Security is the primary consideration for declassification methods. The declassification method must be irreversible; attackers cannot correct and recover the original data from the declassified data to ensure that the true location coordinates are not leaked. Second, precision controllability requirements. The declassification method needs to have controllable precision to adapt to the different precision requirements of different application scenarios. For example, for large-scale vector geographic data, the accuracy index needs to vary depending on the scale (e.g., the accuracy index for vector geographic data at a scale of 1:500,000 is 50-100 meters, and the accuracy index for vector geographic data at a scale of 1:1,000,000 is 100-200 meters). However, for high-precision vector geographic data, according to the "Regulations on the Scope of State Secrets in Surveying and Mapping Geographic Information Management" (Document No. 95), for high-secret data involving military and national security departments, declassification only needs to be reduced to an accuracy of about 10 meters before public use. Therefore, the declassification method should have a flexible and adjustable target declassification accuracy to meet different accuracy requirements in practical applications. Third, usability requirements. The declassified vector geographic data should not affect its usability. Vector geographic data mainly consists of topological information, coordinate information, and attribute information of elements. Declassified data needs to maintain the topological structure, graphic shape, and spatial orientation between elements. Finally, the declassification method needs to be efficient, that is, it needs to have high processing efficiency when declassifying massive amounts of vector data to meet the practical application needs of vector geographic data. Therefore, declassification is not simply a matter of changing location coordinates. The challenge lies in designing a declassification model that can balance the security, controllability, usability, and efficiency of vector geographic data declassification. Summary of the Invention

[0004] This invention aims to propose a controllable declassification method for vector geographic data based on ellipsoidal spatial mapping. Due to the nonlinear and smooth variations of the Earth's ellipsoidal surface, spatial mapping of vector geographic data based on an ellipsoidal model not only makes it difficult to recover the original coordinate information through inverse transformation, but also ensures that the coordinates remain smoothly varied after spatial mapping, meeting the security and usability requirements of the declassified data. Furthermore, by setting different declassification accuracies to adjust the vector geographic data for corresponding scenarios, the requirement for controllable declassification can be met.

[0005] The procedure of this method is as follows: First, an ellipsoidal spatial mapping model is established for vector geographic data based on the Earth ellipsoid and spatial mapping techniques, and the variation range of model parameters is set and analyzed accordingly. Second, offset variation functions in the X and Y axes are designed based on pre-set declassification parameters (i.e., target declassification accuracy and target noise addition) and model parameters, thereby improving the randomness of the declassified data while ensuring data usability. Finally, the vector geographic data is declassified based on the offsets generated by the ellipsoidal spatial mapping model and the offset variation functions in the X and Y axes.

[0006] This invention provides a controllable declassification method for vector geographic data based on ellipsoidal spatial mapping, comprising the following steps:

[0007] Step 1, Establish an ellipsoidal spatial mapping model: First, construct a spatial Cartesian coordinate system based on the center point of the vector geographic data. Then, project the vector geographic data onto the ellipsoidal surface of the Earth ellipsoid according to the mapping height. Finally, map the vector geographic data on the ellipsoidal surface through the mapping angle, thereby achieving data declassification. In this process, the generated model parameters include the major semi-axis, minor semi-axis, mapping height, and mapping angle.

[0008] Step 2, Analyze the range of model parameters: According to the declassification requirements, set two predefined declassification parameters, namely target declassification accuracy and target noise addition. The target declassification accuracy defines the maximum offset distance of the declassified data, while the target noise addition defines the maximum range of noise added to the declassified data. Then, based on the predefined declassification parameters and the ellipsoidal space mapping model established in Step 1, analyze the range of variation of the model parameters.

[0009] Step 3, Design the offset variation function: To ensure the controllability and security of the declassified data, the offset variation function in the X-axis and Y-axis directions is set based on the model parameter range and predefined declassification parameters analyzed in Step 2. In this step, the mapping height and target declassification accuracy are used as the variation targets to design the offset variation function in the X-axis direction, and the mapping angle and target noise addition are used as the variation targets to design the offset variation function in the Y-axis direction.

[0010] Step 4, final declassification: Based on the offset generated by the offset change function in the X and Y axes designed in Step 3, the vector geographic data is perturbed to achieve the final declassification of the data.

[0011] Furthermore, the specific implementation method for establishing the ellipsoidal space mapping model is as follows:

[0012] Step 1.1, assuming the original vector geographic data is Where the x-coordinate is The vertical axis is n is the number of coordinates, and the coordinates of the center point are... Then, based on the center point coordinates Establish an ellipsoidal space mapping model to realize the transformation from two-dimensional data mapping to three-dimensional space;

[0013] Ellipsoidal space mapping model with Establish a spatial Cartesian coordinate system with the center of the ellipsoid, a as the major semi-axis, and b as the minor semi-axis.

[0014]

[0015] Step 1.2: Define the center of the ellipsoid based on the ellipsoidal space mapping model. The plane in question is the latitude circle φ0, and the data... The plane in which it lies is the latitude circle φ1, and the latitude circle φ1 is parallel to the latitude circle φ0. Furthermore, the latitude circle φ1 is... With r as the center and r as the radius, the established ellipsoidal space mapping model has the following properties:

[0016] Property 1: The radius a of the latitude circle φ0 is greater than the radius r of the latitude circle φ1, that is, a>r;

[0017] Property 2: The longer 'a' is, the smoother the decompression coordinate changes after spatial mapping, and the more natural the effect.

[0018] Property 3: All points on the latitude circle φ1 are equidistant from points perpendicularly mapped to the latitude circle φ0, that is, the z coordinates of all points on the latitude circle φ1 are equal;

[0019] Property 4: The point on the latitude circle φ1 with the longest distance from the point perpendicular to the ellipsoidal surface is the center point. Distance is The point with the shortest distance to the perpendicular ellipsoidal surface is the point on the edge of circle φ1, with a distance of 0.

[0020] Step 1.3, De-encryption of the ellipsoidal space mapping model, assuming vector map data points within Points perpendicular to the surface of the ellipsoid And the mapping angle with the latitude circle φ1 is θ is in the same direction as the x-axis, point Offset to point using the following formula Right now

[0021]

[0022] in, and Points Offsets in the X and Y axes; additionally, define According to the point Mapping height to ellipsoidal surface Make changes, and According to the mapping angle To make changes, that is

[0023] Furthermore, the specific implementation method for the step of analyzing the parameter range of the model is as follows:

[0024] Based on properties 1 and 2 obtained in step 1.2, define the major semi-axis. in Vector geographic data The length in the X-axis direction, and δ>1, the minor semi-axis b=a(1-f), where the flattening f is consistent with the flattening of the original data coordinate system;

[0025] In addition, according to the point Offsets in the X and Y axes and It is clear that analysis is needed. and The scope is designed to achieve spatial mapping to the greatest extent possible, and points are calculated using models and properties. The range of variation of the mapped height to the ellipsoidal surface is and points The range of the mapping angle is

[0026] Furthermore, the design steps for the offset variation function in the X-axis direction are as follows:

[0027] Based on the ellipsoidal space mapping model and the range of mapping height changes obtained in step 2, it can be seen that as the point... From the data boundary to data center point Move, its mapped height In the interval [H min H max Steady growth, therefore The changing characteristics of the offset function match the desired effect of the changing function; therefore, the offset function satisfies the changing characteristics of the sine function, i.e.

[0028]

[0029] in, and And H max and H min These are the maximum and minimum values ​​of the mapped height, respectively, according to the function f. s ′ (X) shows that the offset function changes in a smooth asymptotic manner, which can satisfy the availability of decrypted data.

[0030] Furthermore, to further improve the safety of the offset variation function in the X-axis direction, the offset function f is... s ′ (X) Add a perturbation, as shown in the following equation:

[0031]

[0032] Among them, f S (X) is the offset variation function in the X-axis direction, and v s The purpose of perturbing the offset function is to improve the randomness of the function's variation;

[0033] In addition, to ensure the offset change function f S (X) is in the interval [0, ρ] for f s ′ (X) and v s The weighting coefficients λ and γ are assigned as follows:

[0034]

[0035] Among them, v s =γρT, and λ>γ, λ+γ=1, where T is the perturbation function.

[0036] Furthermore, the definition rules for the perturbation function T are as follows:

[0037] Based on the coordinates of the center point Data Divide the region along the X and Y axes to obtain four different regions. Name the four regions from the bottom left corner in a counterclockwise direction to obtain region 1, region 2, region 3 and region 4 respectively.

[0038] First, in order to maintain f′ SThe original form of (X) is obtained by perturbing its changes in different directions. The perturbation functions for regions 1 and 2 are defined as T1 = c1X and T2 = -c2X, and the perturbation functions for regions 3 and 4 are defined as T3 = c3Y and T4 = -c4Y, where c1, c2, c3, and c4 are the perturbation coefficients for regions 1 to 4. Therefore, by perturbing the data in each region in different ways according to the perturbation functions, the data can be improved. Randomness;

[0039] Secondly, the perturbation coefficient for each region is determined to ensure that the perturbation function for each region can be controlled within the interval [0, γρ], while optimizing its perturbation effect. This is because the perturbation v... s The range is [0, γρ], therefore region 1 and region 2 are defined according to X. o Perform a perturbation, i.e., v s (X o The perturbation coefficient is obtained from ) = γρ. Furthermore, regions 3 and 4 are defined according to Y. o Perform a perturbation, i.e., v s (Y o The perturbation coefficient is obtained from ) = γρ.

[0040] Substituting the perturbation functions of different regions into equation (5), we obtain the final offset change function f in the X-axis direction. S (X) and offset As shown in equation (6):

[0041]

[0042] Among them, λ1+γ1=λ2-γ2=1.

[0043] Furthermore, the design steps for the offset variation function in the Y-axis direction are as follows:

[0044] The offset change function f in the Y-axis direction L (Y) is the offset function f′ in the Y-axis direction. l (Y) and the noise variation function f consisting of the target added noise G. G (Y) composition, i.e.

[0045] f L (Y)=f′ l (Y)+f G (Y), (7)

[0046] Offset function f′ in the Y-axis direction l (Y) and the noise variation function f consisting of the target added noise G. G The design principle of (Y) is as follows: When point Located in data When at the boundary, ensure Reaching the maximum, that is Then, during the process of change from the data boundary to the interior, Gradually decrease, and in the data center point The time mapping angle is at its minimum, that is Then, the mapping angle is gradually increased until it is restored to its original value. Therefore, based on the mapping angle θ, the offset function f′ in the y-axis direction l (Y) and noise variation function f G (Y) satisfies the cosine function, that is, the offset change function f in the Y direction. L (Y) is shown below:

[0047]

[0048] in, and Y max and Y min Data respectively Maximum and minimum Y values, Y o For data center points The Y value, and f′ l The mapping angle between (Y) and G.

[0049] Furthermore, to ensure that the range of the offset change function in the Y direction is controlled within the interval [0, ρ], the mapping angle is determined according to the mapping angle range. and Defined as:

[0050]

[0051] Where G∈(0,ρ), n is the number of coordinates. For data The height of each point mapped to the ellipsoidal surface, i.e.

[0052] Based on the ellipsoidal space mapping model and the offset variation function in the Y direction shown in equation (9), the point The final offset in the y-axis direction is:

[0053]

[0054] Furthermore, the specific implementation method for performing the final declassification process is as follows:

[0055] Based on the ellipsoidal space mapping model, and combining the offset variation functions in the X-axis and Y-axis directions, the point Decryption is performed using the following formula: Right now

[0056]

[0057] Where ξ is a random coefficient, with a value of 1 or -1. Point The final offset in the X-axis direction, Point The final offset in the Y-axis direction;

[0058] Therefore, the original vector geographic data can be obtained through formula (11). Declassification refers to the process of declassifying data. in

[0059] This invention also provides a controllable declassification system for vector geographic data based on ellipsoidal spatial mapping, comprising the following steps:

[0060] The ellipsoidal spatial mapping model building module is used to establish an ellipsoidal spatial mapping model: First, a spatial Cartesian coordinate system is constructed based on the center point of the vector geographic data. Then, the vector geographic data is projected onto the ellipsoidal surface of the Earth ellipsoid according to the mapping height. Finally, the vector geographic data on the ellipsoidal surface is mapped through the mapping angle, thereby achieving data declassification. In this process, the generated model parameters include the major semi-axis, minor semi-axis, mapping height, and mapping angle.

[0061] The model parameter range analysis module is used to analyze the range of model parameters: According to the declassification requirements, two predefined declassification parameters are set, namely target declassification accuracy and target added noise. The target declassification accuracy defines the maximum offset distance of the declassified data, while the target added noise defines the maximum range of noise added to the declassified data. Then, based on the predefined declassification parameters and the established ellipsoidal space mapping model, the variation range of the model parameters is analyzed.

[0062] The offset variation function design module is used to design offset variation functions: To ensure the controllability and security of declassified data, offset variation functions in the X-axis and Y-axis directions are set based on the analyzed model parameter range and predefined declassification parameters. In this step, the mapping height and target declassification accuracy are used as variation targets to design the offset variation function in the X-axis direction, and the mapping angle and target noise addition are used as variation targets to design the offset variation function in the Y-axis direction.

[0063] The declassification module is used for the final declassification process: based on the offset generated by the offset change function in the designed X-axis and Y-axis directions, the vector geographic data is perturbed to achieve the final declassification of the data.

[0064] Compared with existing technologies, the advantages of this invention are as follows: This invention proposes a controllable declassification method for vector geographic data based on ellipsoidal spatial mapping. This method utilizes an ellipsoidal spatial mapping model constructed using the Earth ellipsoid and spatial mapping techniques, thereby ensuring the nonlinearity and irreversibility of the declassification process. Furthermore, by pre-setting declassification parameters to determine the range of model parameter variations, and designing offset variation functions in the X and Y axes, this approach ensures high security and controllable accuracy of the declassified data, effectively preserving the consistency of the declassified data's topological structure, geometry, and spatial orientation. Therefore, this invention can effectively prevent the exposure of real coordinate information and meets the confidentiality requirements of vector geographic data, facilitating the public sharing and use of vector data. Compared with existing technologies, this invention provides higher security, availability, controllability, and declassification efficiency. Attached Figure Description

[0065] Figure 1 This is a general schematic diagram of the decryption method according to an embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of the ellipsoidal space mapping model according to an embodiment of the present invention;

[0067] Figure 3 This is a schematic diagram of the region division of the design offset change function according to an embodiment of the present invention;

[0068] Figure 4 This is a diagram illustrating the decryption effect of the present invention. Detailed Implementation

[0069] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0070] like Figure 1 As shown, the present invention provides a controllable declassification method for vector geographic data based on ellipsoidal spatial mapping, which is specifically implemented in four steps:

[0071] Step 1: Establish an ellipsoidal spatial mapping model. First, construct a spatial Cartesian coordinate system based on the center point of the vector geographic data. Then, project the vector geographic data onto the ellipsoidal surface of the Earth ellipsoid according to the mapping height. Finally, map the vector geographic data on the ellipsoidal surface through the mapping angle, thereby achieving data declassification. The model parameters generated in this process include the major semi-axis, minor semi-axis, mapping height, and mapping angle. Step 1.1, assuming the original vector geographic data is... Where the x-coordinate is The vertical axis is n is the number of coordinates, and the coordinates of the center point are... Then, based on the center point coordinates Establish an ellipsoidal space mapping model, such as Figure 2 As shown, this is to achieve the transformation from two-dimensional data mapping to three-dimensional space.

[0072] Furthermore, to ensure that the declassified data retains a smooth variation after spatial mapping through the model, the ellipsoidal spatial mapping model uses... Establish a spatial Cartesian coordinate system with the center of the ellipsoid, a as the major semi-axis, and b as the minor semi-axis.

[0073]

[0074] Step 1.2: Define the center of the ellipsoid based on the ellipsoidal space mapping model. The plane in question is the latitude circle φ0, and the data... The plane in question is the latitude circle φ1, and latitude circle φ1 is parallel to latitude circle φ0. Furthermore, latitude circle φ1 is... Let r be the center and r be the radius. Therefore, the established ellipsoidal space mapping model is as follows: Figure 2 As shown, the model has the following properties:

[0075] Property 1: The radius a of the latitude circle φ0 is greater than the radius r of the latitude circle φ1, that is, a>r;

[0076] Property 2: The longer 'a' is, the smoother the decompression coordinate changes after spatial mapping, and the more natural the effect.

[0077] Property 3: All points on the latitude circle φ1 are equidistant from points perpendicularly mapped to the latitude circle φ0, that is, the z coordinates of all points on the latitude circle φ1 are equal;

[0078] Property 4: The point on the latitude circle φ1 with the longest distance from the point perpendicular to the ellipsoidal surface is the center point. Distance is The point with the shortest distance to the perpendicular mapping surface of the ellipsoid is a point on the edge of circle φ1 (e.g., point). ), the distance is 0.

[0079] Step 1.3, De-encryption of the ellipsoidal spatial mapping model. Assume vector geographic data... points within Points perpendicular to the surface of the ellipsoid And the mapping angle with the latitude circle φ1 is (Assuming θ is in the same direction as the x-axis), point Offset to point using the following formula Right now

[0080]

[0081] in, and Points Offsets in the X-axis and Y-axis directions. Furthermore, this invention defines... According to the point Mapping height to ellipsoidal surface Make changes, and According to the mapping angle To make changes, that is

[0082] Step 2: Analyze the range of model parameters. The declassification process needs to meet two requirements. First, different declassification accuracies should be set according to the specific application scenario to process the data. Second, random noise should be introduced, which can produce different declassification results at the same declassification accuracies, thereby increasing the randomness of the declassified data. Therefore, based on the above declassification requirements, two predefined declassification parameters are set: target declassification accuracy and target added noise. The target declassification accuracy defines the maximum offset distance of the declassified data, while the target added noise defines the maximum range of noise added to the declassified data. Then, based on the predefined declassification parameters and the ellipsoidal space mapping model established in Step 1, the range of model parameter variations is analyzed. To ensure the usability of the declassified data, suitable model parameters need to be selected. Based on properties 1 and 2 obtained in Step 1.2, the semi-major axis is defined. in Vector geographic data The length in the X-axis direction, and δ>1. The minor semi-axis b=a(1-f), where the flattening f is consistent with the flattening of the vector geographic data coordinate system. For example, if the coordinate system of the vector geographic data is WGS-84, then the flattening value of the WGS-84 ellipsoid is used, i.e., f=1 / 298.2572.

[0083] In addition, according to Figure 2 Points in Offsets in the X and Y axes and It is clear that analysis is needed. and The range is designed to achieve spatial mapping to the greatest extent possible. Points can be calculated using the model and properties. The range of variation of the mapped height to the ellipsoidal surface is and points The range of the mapping angle is

[0084] Step 3: Design the offset variation function. To ensure the controllability and security of the declassified data, the offset variation functions in the X and T axes are set based on the model parameter range analyzed in Step 2 and the predefined declassification parameters (i.e., target declassification accuracy and target noise addition). In this step, the mapping height and target declassification accuracy are used as the variation targets to design the offset variation function F in the X-axis direction. h (X), and the mapping angle and target noise are added as the changing targets to design the offset change function F in the Y-axis direction. θ (Y). The desired effect of the offset variation function in the X and Y axes is as follows: when point The X coordinate is located in the data When defining boundaries, to ensure consistent topological relationships at the boundary intersections of the declassified areas, it is necessary to maintain unchanged positional accuracy at the boundaries, thereby preventing geographical features from crossing or shifting at the boundary intersections within the declassified areas; when points When internal changes occur, the positional accuracy should be reduced according to the target's decryption accuracy, thereby increasing the complexity of the decryption process and the difficulty of cracking.

[0085] The design steps for the offset variation function in the X-axis direction are as follows.

[0086] Step 3.1, according to Figure 2 As can be seen from the range of changes in the mapped height obtained in step 2, with the point From the data boundary to data center point Move, its mapped height In the interval [H min H max Steady growth, therefore The changing characteristics of the offset function conform to the effect that the function intends to achieve. Therefore, the offset function satisfies the change of the sine function, that is...

[0087]

[0088] in, and And H max and H min These are the maximum and minimum values ​​of the mapped height, respectively. According to the function f′ s (X) shows that the offset function changes in a smooth asymptotic manner, which can satisfy the availability of decrypted data.

[0089] Step 3.2, to further improve the safety of the offset change function in the X-axis direction, the offset function f′ is... s (X) Add a perturbation, as shown in the following equation.

[0090]

[0091] Among them, f S (X) is the offset variation function in the X-axis direction, and v s The purpose of perturbing the offset function is to improve the randomness of the function's variation.

[0092] In addition, to ensure the offset change function f S (X) is within the interval [0, ρ], for f′ s (X) and v s The weighting coefficients λ and γ are assigned as shown in the following formula.

[0093]

[0094] Among them, v s =γρT, and λ>γ, λ+γ=1. T is the perturbation function.

[0095] Step 3.3, the definition rules for the disturbance function T are as follows. Based on the center point coordinates... Data Divide the data along the X and Y axes to obtain the following: Figure 3 The four regions shown are of the same size. Starting from the bottom left corner, they are named counterclockwise as region 1, region 2, region 3, and region 4.

[0096] First, in order to maintain f′ s The original form of (X) is obtained by perturbing its changes in different directions. The perturbation functions for regions 1 and 2 are defined as T1 = c1X and T2 = -c2X, and the perturbation functions for regions 3 and 4 are defined as T3 = c3Y and T4 = -c4Y. Here, c1, c2, c3, and c4 are the perturbation coefficients for regions 1 to 4. Therefore, based on the perturbation functions, the data within each region can be perturbed in different ways, thereby improving the data... The randomness.

[0097] Secondly, it is necessary to determine the perturbation coefficient for each region to ensure that the perturbation function for each region can be controlled within the interval [0, γρ], while optimizing its perturbation effect. Due to the perturbation v... s The range is [0, γρ], therefore region 1 and region 2 are defined according to X. o Perform a perturbation, i.e., v s (X o The perturbation coefficient is obtained from ) = γρ. Furthermore, regions 3 and 4 are defined according to Y. o Perform a perturbation, i.e., v s (Y i The perturbation coefficient is obtained from ) = γρ.

[0098] Step 3.4: Substitute the perturbation functions of different regions into equation (5) to obtain the final offset change function F in the X-axis direction. h (X) and offset As shown in equation (6).

[0099]

[0100]

[0101] Among them, λ1+γ1=λ2-γ2=1.

[0102] The design steps for the offset variation function in the Y-axis direction are as follows.

[0103] Step 3.5, the offset change function f in the Y-axis direction θ (Y) is the offset function f′ in the Y-axis direction. l (Y) and the noise variation function f consisting of the target added noise G. G (Y) composition, i.e.

[0104] f θ (Y)=f′ l (Y)+f G (Y), (7)

[0105] Step 3.6, the offset function f′ in the Y-axis direction l (Y) and the noise variation function f consisting of the target added noise G. G The design principle of (Y) is as follows. Figure 2 As shown, when the point Located in data When at the boundary, it is necessary to ensure Reaching the maximum, that is Then, during the process of change from the data boundary to the interior, Gradually decrease, and in the data center point The time mapping angle is at its minimum, that is Then, the mapping angle is gradually increased until it is restored to its original value. Therefore, based on the mapping angle θ, the offset function f′ in the y-axis direction l (Y) and noise variation function f G (Y) satisfies the cosine function, that is, the offset change function f in the Y direction. θ (Y) is shown below.

[0106]

[0107] in, and Y max and Ymin Data respectively Maximum and minimum Y values, Y O For data center points The Y value, and f′ l The mapping angle between (Y) and G.

[0108] Step 3.7: To ensure that the range of the offset change function in the Y direction is controlled within the interval [0, ρ], the mapping angle is determined according to the mapping angle range. and Defined as:

[0109]

[0110] Where G∈(0,ρ), n is the number of coordinates. For data The height of each point mapped to the ellipsoidal surface, i.e.

[0111] Step 3.8, based on the ellipsoidal space mapping model and the offset variation function in the Y direction shown in equation (9), the point The final offset in the Y-axis direction is:

[0112]

[0113] Step 4: Final Declassification Processing. Based on the offset generated by the offset transformation functions designed in Step 3 along the X and Y axes, the vector geographic data is perturbed as follows to achieve final declassification. Based on the offset shown in Equation (2), and combined with the transformation functions designed in Equations (6) and (10), the points... Decryption is performed using the following formula: Right now

[0114]

[0115] Where ξ is the direction coefficient, and its value is 1 or -1.

[0116] Therefore, through this invention, raw vector geographic data can be... Declassification refers to the process of declassifying data. in

[0117] The vector geographic data complete decryption process involved in this invention is as follows: algorithm, and the decryption effect is as follows: Figure 4 As shown.

[0118]

[0119]

[0120] This invention also provides a controllable declassification system for vector geographic data based on ellipsoidal spatial mapping, comprising the following modules:

[0121] The ellipsoidal spatial mapping model building module is used to establish an ellipsoidal spatial mapping model: First, a spatial Cartesian coordinate system is constructed based on the center point of the vector geographic data. Then, the vector geographic data is projected onto the ellipsoidal surface of the Earth ellipsoid according to the mapping height. Finally, the vector geographic data on the ellipsoidal surface is mapped through the mapping angle, thereby achieving data declassification. In this process, the generated model parameters include the major semi-axis, minor semi-axis, mapping height, and mapping angle.

[0122] The model parameter range analysis module is used to analyze the range of model parameters: According to the declassification requirements, two predefined declassification parameters are set, namely target declassification accuracy and target added noise. The target declassification accuracy defines the maximum offset distance of the declassified data, while the target added noise defines the maximum range of noise added to the declassified data. Then, based on the predefined declassification parameters and the established ellipsoidal space mapping model, the variation range of the model parameters is analyzed.

[0123] The offset variation function design module is used to design offset variation functions: To ensure the controllability and security of declassified data, offset variation functions in the X-axis and Y-axis directions are set based on the analyzed model parameter range and predefined declassification parameters. In this step, the mapping height and target declassification accuracy are used as variation targets to design the offset variation function in the X-axis direction, and the mapping angle and target noise addition are used as variation targets to design the offset variation function in the Y-axis direction.

[0124] The declassification module is used for the final declassification process: based on the offset generated by the offset change function in the designed X-axis and Y-axis directions, the vector geographic data is perturbed to achieve the final declassification of the data.

[0125] The specific implementation methods of each module and the corresponding steps are not described in this invention.

[0126] The above description, in conjunction with the preferred embodiments, provides a further detailed explanation of the present invention and should not be construed as limiting the specific implementation of the invention to these descriptions. Those skilled in the art should understand that various modifications to the details may be made without departing from the scope defined by the appended claims, and all such modifications should be considered to fall within the protection scope of the present invention.

Claims

1. A controllable declassification method for vector geographic data based on ellipsoidal spatial mapping, characterized in that, Includes the following steps: Step 1, Establish an ellipsoidal spatial mapping model: First, construct a spatial Cartesian coordinate system based on the center point of the vector geographic data. Then, project the vector geographic data onto the ellipsoidal surface of the Earth ellipsoid according to the mapping height. Finally, map the vector geographic data on the ellipsoidal surface through the mapping angle to achieve data declassification. In this process, the generated model parameters include the major semi-axis, minor semi-axis, mapping height, and mapping angle. Step 2, Analyze the range of model parameters: According to the declassification requirements, set two predefined declassification parameters, namely target declassification accuracy and target noise addition. The target declassification accuracy defines the maximum offset distance of the declassified data, while the target noise addition defines the maximum range of noise added to the declassified data. Then, based on the predefined declassification parameters and the ellipsoidal space mapping model established in Step 1, analyze the range of variation of the model parameters. Step 3, Design the offset variation function: To ensure the controllability and security of the declassified data, the offset variation function is set based on the model parameter range analyzed in Step 2 and the predefined declassification parameters. shaft and The offset variation function in the axial direction, in this step, uses the mapping height and target decryption accuracy as variation targets to design... The offset variation function along the axis, and the mapping angle and target noise added as variation targets, are used for design. Offset variation function in the axial direction; Step 4, Final Declassification Process: Based on the design in Step 3 shaft and The offset generated by the offset change function in the axial direction perturbs the vector geographic data, thereby achieving the final declassification of the data.

2. The controllable declassification method for vector geographic data based on ellipsoidal spatial mapping as described in claim 1, characterized in that: The specific implementation method for establishing the ellipsoidal space mapping model is as follows. Step 1.1, assuming the original vector geographic data is The x-axis is The vertical axis is , The coordinates are the number of coordinates, and the coordinates of the center point are... Then, based on the center point coordinates Establish an ellipsoidal space mapping model to realize the transformation from two-dimensional data mapping to three-dimensional space; Ellipsoidal space mapping model with Center of the ellipsoid For the long half-shaft, Establish a spatial Cartesian coordinate system for the minor semi-axis, i.e. Step 1.2: Define the center of the ellipsoid based on the ellipsoidal space mapping model. The plane in question is a circle of latitude. ,data The plane in question is a circle of latitude. And latitude circle Circle of latitude Parallel, in addition, latitude circle by With the center of the circle, Since the radius is , the established ellipsoidal space mapping model has the following properties: Property 1: Latitude Circle radius Larger than latitude circle radius ,Right now ; Property 2: If The longer the length, the smoother the decryption coordinate changes after spatial mapping, and the more natural the effect. Property 3: Latitude Circle All points on the circle of latitude Points mapped vertically are equidistant, i.e., the latitude circle. All points on The coordinates are equal; Property 4: In the latitude circle The center point is the point whose maximum distance is between the top and the point perpendicular to the ellipsoidal surface. The distance is ; The point with the shortest distance to the point perpendicular to the ellipsoidal surface is a circle. Points on the edge are 0 units away; Step 1.3, De-encryption of the ellipsoidal space mapping model, assuming vector map data points within Points perpendicular to the surface of the ellipsoid and with latitude circle The mapping angle is , and Same axis, point Offset to point using the following formula ,Right now in, and Points exist Axial direction and Offset in the axial direction; furthermore, define According to the point Mapping height to ellipsoidal surface Make changes, and According to the mapping angle To make changes, that is .

3. The controllable declassification method for vector geographic data based on ellipsoidal spatial mapping as described in claim 2, characterized in that: The specific implementation method for the step of analyzing the parameter range of the model is as follows. Based on properties 1 and 2 obtained in step 1.2, define the major semi-axis. ,in Vector geographic data exist The length in the axial direction, and short half-shaft flatness The flattening is consistent with the original data coordinate system; In addition, according to the point exist Axial direction and Offset in the axial direction and It is clear that analysis is needed. and The scope is designed to achieve spatial mapping to the greatest extent possible, and points are calculated using models and properties. The range of variation of the mapped height to the ellipsoidal surface is and points The range of the mapping angle is .

4. The controllable declassification method for vector geographic data based on ellipsoidal spatial mapping as described in claim 1, characterized in that: The design steps for the offset variation function in the axial direction are as follows: Based on the ellipsoidal space mapping model and the range of mapping height changes obtained in step 2, it can be seen that as the point... From the data boundary to data center point Move, its mapped height In the interval Steady growth, therefore The changing characteristics of the offset function match the desired effect of the changing function; therefore, the offset function satisfies the changing characteristics of the sine function, i.e. in, and ,and and These are the maximum and minimum values ​​of the mapped height, respectively, according to the function. It can be seen that the offset function changes in a smooth asymptotic manner, which can satisfy the usability of decrypted data.

5. The controllable declassification method for vector geographic data based on ellipsoidal spatial mapping as described in claim 4, characterized in that: To further improve The safety of the offset variation function in the axial direction, for the offset function Add the perturbation as shown in the following equation: in, for The offset variation function in the axial direction, and The purpose of perturbing the offset function is to improve the randomness of the function's variation; In addition, to ensure the offset change function The range is in the interval Inside, to and Assign weight coefficients and As shown in the following formula: in, ,and , , This is the perturbation function.

6. The controllable declassification method for vector geographic data based on ellipsoidal spatial mapping as described in claim 5, characterized in that: perturbation function The definition rules are as follows: Based on the coordinates of the center point Data conduct shaft and The axis is divided into four different regions. Starting from the lower left corner, the four regions are named in a counterclockwise direction, resulting in Region 1, Region 2, Region 3 and Region 4. First, in order to maintain The original form of change is obtained, and its change result is perturbed in different directions. The perturbation functions of region 1 and region 2 are defined as follows: and The perturbation functions for regions 3 and 4 are defined as follows: and ,in, , , and The perturbation coefficients are for regions 1 to 4; therefore, the data in each region is perturbed in different ways according to the perturbation function, thereby improving the data... Randomness; Secondly, determine the perturbation coefficient for each region to ensure that the perturbation function for each region is controlled within the interval. Internally, this allows for optimal perturbation, due to the perturbation... The range is Therefore, region 1 and region 2 are defined according to To perform a disturbance, i.e. The resulting perturbation coefficient Furthermore, regions 3 and 4 are defined according to... To perform a disturbance, i.e. The resulting perturbation coefficient ; Substituting the perturbation functions of different regions into equation (5), we obtain the final result. Offset variation function in the axial direction and offset As shown in equation (6): in, .

7. The controllable declassification method for vector geographic data based on ellipsoidal spatial mapping as described in claim 1, characterized in that: The design steps for the offset variation function in the axial direction are as follows: Offset variation function in the axial direction It is by Offset function in the axial direction and adding noise to the target The noise variation function constituted Composition, that is Offset function in the axial direction and adding noise to the target The noise variation function constituted The design principle is as follows: when point Located in data When at the boundary, ensure Reaching the maximum, that is Then, during the process of changes from the data boundary to the interior, Gradually decrease, and in the data center point The time mapping angle is at its minimum, that is Then, the mapping angle is gradually increased until it is restored to its original value. ; Therefore, according to the mapping angle , Offset function in the axial direction and noise variation function Satisfy the cosine function, that is Offset variation function in direction As shown below: in, and , and Data respectively Maximum and minimum value, For data center points of value, and They are respectively and The mapping angle.

8. The controllable declassification method for vector geographic data based on ellipsoidal spatial mapping as described in claim 7, characterized in that: To ensure The range of the offset variation function in the direction is controlled within the interval Based on the range of mapped angles, the mapped angles will be... and Defined as: in, , Number of coordinates For data The height of each point mapped to the ellipsoidal surface, i.e. ; Based on the ellipsoidal space mapping model and Equation (9) Offset function in direction, point exist The final offset in the axial direction is:

9. The controllable declassification method for vector geographic data based on ellipsoidal spatial mapping as described in claim 1, characterized in that: The specific implementation method for performing the final declassification process is as follows: Based on the ellipsoidal space mapping model, and combined with the formula Offset variation function in the axial direction and Offset variation function in the axial direction, point Decryption is performed using the following formula: ,Right now in, The random coefficient has a value of or , Point exist The final offset in the axial direction, Point exist The final offset in the axial direction; Therefore, the original vector geographic data is obtained through formula (11). Declassification refers to the process of declassifying data. ,in , .

10. A controllable declassification system for vector geographic data based on ellipsoidal spatial mapping, characterized in that, Includes the following modules: The ellipsoidal spatial mapping model building module is used to build an ellipsoidal spatial mapping model: First, a spatial Cartesian coordinate system is constructed based on the center point of the vector geographic data. Then, the vector geographic data is projected onto the ellipsoidal surface of the Earth ellipsoid according to the mapping height. Finally, the vector geographic data on the ellipsoidal surface is mapped through the mapping angle, thereby achieving data declassification. In this process, the generated model parameters include the major semi-axis, minor semi-axis, mapping height, and mapping angle. The model parameter range analysis module is used to analyze the range of model parameters: According to the declassification requirements, two predefined declassification parameters are set, namely target declassification accuracy and target added noise. The target declassification accuracy defines the maximum offset distance of the declassified data, while the target added noise defines the maximum range of noise added to the declassified data. Then, based on the predefined declassification parameters and the established ellipsoidal space mapping model, the variation range of the model parameters is analyzed. The offset transformation function design module is used to design the offset transformation function: to ensure the controllability and security of the declassified data, it sets the offset transformation function based on the analyzed model parameter range and predefined declassification parameters. shaft and The offset variation function in the axial direction, in this step, uses the mapping height and target decryption accuracy as variation targets to design... The offset variation function along the axis, and the mapping angle and target noise added as variation targets, are used for design. Offset variation function in the axial direction; Declassification processing module, used for final declassification processing: according to the design shaft and The offset generated by the offset change function in the axial direction perturbs the vector geographic data, thereby achieving the final declassification of the data.

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