Privacy protection unmanned aerial vehicle distribution statistical method based on improved distributed point function

Abstract

The invention belongs to the field of privacy protection data statistics, and particularly relates to a privacy protection unmanned aerial vehicle distribution statistical method based on an improved distributed point function, and the specific process comprises the steps: Gray code and key generation: carrying out the region division of a three-dimensional space through employing a multi-layer octree, and carrying out the unique coding of each layer of region through combining with the Gray code; each UAV generates a secret key through an improved distributed point function based on the position code of the UAV in the three-dimensional space, and sends the secret key to the two corresponding servers; data processing: the server receives a secret key from each UAV, analyzes the secret key layer by layer and calculates a UAV position distributed statistical result in combination with a correction word; and data aggregation: the requester receives the data processing result of the server and aggregates the data processing result to obtain the spatial distribution of the unmanned aerial vehicle.

Description

Technical Field

[0001] This invention belongs to the field of privacy-preserving data statistics, specifically relating to a privacy-preserving drone distribution statistics method based on an improved distributed point function. Background Technology

[0002] Unmanned aerial vehicles (UAVs) are playing an increasingly important role in mission recommendation systems, particularly in critical areas such as logistics, emergency rescue, and environmental monitoring. UAV-based mission recommendation systems require processing large amounts of three-dimensional spatial data, which includes the following core elements: location data, mission data, and performance data. Location data includes the precise three-dimensional coordinates (longitude, latitude, and altitude) and real-time flight trajectory information of the UAVs. This data not only reflects the spatial location of the UAVs but also their motion state and flight mode, serving as the foundation for mission recommendation and path planning. Mission data covers the spatial distribution characteristics and execution requirements of the tasks to be performed, including multi-dimensional information such as the geographical scope, time constraints, and resource requirements. This data directly determines the mission allocation strategy and execution effectiveness. Performance data involves the UAVs' capability indicators and historical performance records, including key indicators such as flight performance parameters, mission completion efficiency, and equipment reliability. This data is crucial for evaluating the suitability of UAVs and optimizing mission allocation. This data contains a large amount of sensitive information: on the one hand, it involves trade secrets, such as logistics delivery routes and monitoring area layouts; on the other hand, it involves security information, such as the precise location and flight capability parameters of the UAVs. How to achieve efficient UAV distribution statistics while protecting privacy has become a key technical challenge.

[0003] With the widespread adoption of unmanned aerial vehicles (UAVs) in various fields such as logistics, environmental monitoring, and agriculture, the need for effective management of UAV deployments has increased significantly. Analyzing the spatial distribution of UAVs is crucial for effective resource allocation, operational efficiency, and airspace conflict prevention, as it not only provides valuable insights into coverage areas, density, and airspace utilization, but also enables better management of complex multi-UAV operations in urban environments.

[0004] However, performing such spatial distribution analysis typically requires UAVs to upload their precise location data to a central server, which raises serious privacy concerns. In practical applications, when deploying UAVs in restricted areas to perform sensitive missions, directly sharing their location information could lead to the risk of being tracked or monitored. Therefore, UAV location data needs to be encrypted before being uploaded to the server. Besides privacy issues, UAVs often face limitations in computing power, storage capacity, and energy resources, making it crucial to reduce computational and communication overhead. Furthermore, UAVs frequently operate in complex environments, and accurate location data is essential for optimizing resource allocation, flight planning, and coverage. For example, inaccurate spatial statistics can lead to inefficient routing, increased flight time, and energy consumption. More importantly, without accurate data, UAVs may risk entering restricted areas or flying too close to their targets, potentially resulting in collisions or mission failure. In conclusion, providing efficient and accurate spatial distribution statistics while ensuring UAV location privacy has become a core challenge that must be addressed.

[0005] Existing work has achieved statistics while protecting user data privacy. For example, the papers "Federated heavyhitters discovery with differential privacy" and "Practical locally private heavyhitters" use a single server to implement frequent item data for clients based on differential privacy. This approach trades off between practicality and privacy, as it exposes some private data information from the client to the server. Other works, such as "Efficient secure three-party sorting with applications to data analysis and heavyhitters" and "Vogue: Faster computation of private heavyhitters," construct privacy-preserving frequent item protocols after secure sorting under secure multi-party computation. While computationally efficient, these methods incur significant communication overhead. To further reduce overhead, "Lightweight techniques for private heavyhitters" uses two non-collusive servers, allowing efficient evaluation of prefix-based strings by reusing the same DPF key. Building on this work, "Plasma: Private, lightweight aggregated statistics against malicious adversaries" enhances security by adding additional servers. However, these works only consider one-dimensional data statistics, such as string or sequence information, and lack research on spatial distribution statistics.

[0006] With the widespread application of UAVs in logistics, environmental monitoring, agriculture, and urban management, the demand for statistical analysis of UAV spatial distribution is gradually increasing. These statistical analyses are crucial for optimal resource allocation, flight path planning, and airspace conflict prevention. However, existing technologies for UAV spatial distribution statistics have the following significant shortcomings: First, insufficient privacy protection: Many current statistical methods rely on drones uploading precise location information to a centralized server, which can lead to the leakage of sensitive location information, especially in scenarios where drones are operating in confidential areas. Although some methods use differential privacy technology on a single server for data protection, privacy may still be compromised due to the risk of information leakage in the statistical results.

[0007] Second, statistical efficiency is low: Existing technologies for handling spatial distribution statistics mostly employ methods based on full tree traversal or point-by-point calculation. As the statistical area or the number of drones increases, the computational complexity and communication overhead grow exponentially, making it difficult to meet the needs of real-time statistics. This burden on computational and storage resources is particularly severe in complex statistical scenarios in three-dimensional space.

[0008] Third, there is a lack of optimization for three-dimensional spatial distribution: most existing statistical schemes are designed for one-dimensional or two-dimensional data, and lack effective segmentation and encoding strategies for UAV position information with three-dimensional spatial distribution, resulting in low efficiency of the statistical process and difficulty in meeting the needs of accurate three-dimensional analysis. Summary of the Invention

[0009] In view of this, the objective of this invention is to provide a privacy-preserving drone distribution statistics method based on an improved distributed point function, which can achieve privacy and efficient distribution statistics without revealing the specific location of the drone.

[0010] The technical solution for implementing the present invention is as follows: A privacy-preserving drone distribution statistics method based on an improved distributed point function is proposed, the specific process of which is as follows: Gray code and key generation: The three-dimensional space is divided into regions using a multi-level octree, and each region is uniquely encoded using Gray codes; each UAV generates a key based on its position in the three-dimensional space using an improved distributed point function, and sends it to two corresponding servers; Data processing: The server receives the key from each UAV, and calculates the distributed statistical results of UAV location by parsing the key layer by layer and combining it with the correction words; Data aggregation: The requester receives the data processing results from the server and aggregates them to obtain the spatial distribution of drones.

[0011] Optionally, the specific process of generating a key by improving the distributed point function as described in this invention is as follows: For each server, a seed and control bits are randomly initialized; a pseudo-random generator is used to generate path values ​​for the corresponding server, where the path values ​​represent branches of an octree; and the correct path and the incorrect path are determined based on the current position information. Calculate the correction words corresponding to the correct path and the incorrect path, update the seed and control bits, and accumulate the correction words of the left and right paths of this layer to form the correction words of this layer; A key pair is generated by accumulating the correction words, initial seed, and control bits of all layers.

[0012] Optionally, the present invention improves the distributed point function. The specific process of generating the key is as follows First, randomly initialize the seeds for both servers. and control bits The subscript "0" corresponds to the first server, and the subscript "1" corresponds to the second server.

[0013] For each layer Through a pseudo-random generator For server Generate path values, :

[0014] Among them, the path is as follows Represents a branch of an octree; Secondly, based on the current location information ,Sure" Path and " "Path, i.e., correct path and incorrect path; calculate the correct words for the correct path and incorrect path, and combine the correct words calculated for each path in this layer into the correct words for this layer." ; Finally, by accumulating the correction words of all layers and the initial seed Generate key pairs: .

[0015] Optionally, the correct words for calculating the correct and incorrect paths as described in this invention are specifically as follows: For each erroneous path, the corrective term is calculated as follows:

[0016] in, and This indicates partial correction words on the error path. and These represent the seeds on the erroneous paths in the two servers. and These represent the control bits on the error path in each of the two servers; The corrected term for the correct path is:

[0017] in, The correct word indicates the correct path.

[0018] Optionally, the present invention updates the current control bit and seed:

[0019] in, Indicates the control bits and seed on the correct path.

[0020] Optionally, the correction words of the last layer described in this invention The calculation formula is:

[0021] in, Indicates initialization parameters, This represents the control bit at layer n in server 1. Optionally, the data processing described in this invention employs... The algorithm implementation process is as follows: First, from the key Extracting the initial seed Control bit and the correction words for each layer ; Then, for each layer Using a pseudo-random generator Based on the seed of the previous layer and correction words Calculate pseudo-random values: Encode according to the current layer position. Select the seed and control bits for the corresponding path; Finally, through the last layer of seeds and control bits With final correction words Combine and calculate the statistical results: .

[0022] Optionally, the pseudo-random generator described in this invention and in the pseudo-random generator A pseudo-random group element is defined in the code to transform a length of... Random string: .

[0023] Optionally, when the location of the UAV changes, the present invention updates its location information by generating two key-sharing pairs.

[0024] Optionally, the present invention updates its location information by generating two key-sharing pairs, the specific process of which is as follows: First, the UAV generates a key share to offset the old location and sends it to the server to remove its influence from the statistics of the original location; Then, the UAV generates new key shares representing the updated location information and sends these key shares to the server for aggregation.

[0025] Beneficial effects: First, it offers stronger privacy protection: Many current distributed statistical solutions (such as differential privacy technology or encrypted indexing technology based on a single server) are at risk of privacy leakage, especially in the statistical results where some location information may be leaked. This solution, through an improved distributed point function combined with a non-cooperative computing architecture of two or more servers, ensures that even if a single server is attacked, the specific location information of the drone cannot be inferred, thus achieving stronger privacy protection and making it suitable for statistical scenarios involving sensitive data.

[0026] Second, statistical efficiency is significantly improved: Most existing solutions rely on complete tree traversal or full-area computation in high-dimensional spaces, resulting in high computational complexity, especially in scenarios with multiple UAVs or high-density distribution, making it difficult to meet the needs of real-time statistics. This application, based on an octree-based 3D space partitioning method and Gray code encoding technology, effectively reduces the depth of the tree. Combined with an improved eDPF algorithm, it greatly reduces computational complexity and communication overhead, and improves statistical response speed, showing significant advantages, especially in large-scale 3D scenes.

[0027] Third, communication overhead is significantly reduced: traditional distributed point function-based schemes usually require handling large amounts of keys and data, resulting in high communication overhead and making them unsuitable for high-density scenarios.

[0028] By optimizing the distributed point function algorithm and adopting an octree structure, this application significantly reduces the key length and communication data volume, demonstrating significant resource savings in statistical scenarios with high spatial resolution. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of a distributed point function. Detailed Implementation

[0031] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0032] It should be noted that, in the absence of conflict, the following embodiments and features can be combined with each other; and, based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0033] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0034] This invention proposes a three-dimensional spatial distribution statistical method based on octrees and an improved distributed point function (eDPF). The octree achieves efficient spatial region partitioning and encoding, while Gray code indexing optimizes statistical accuracy and efficiency. Simultaneously, the eDPF algorithm generates a distributed key to ensure the privacy and security of UAV location information during the statistical process. Furthermore, a two-server non-cooperative computing mechanism is designed to significantly reduce computational and communication overhead, supporting real-time dynamic updates and efficient statistics in multi-UAV scenarios. This addresses the shortcomings of existing technologies in privacy protection, statistical efficiency, and three-dimensional spatial processing. The method mainly includes three steps: Gray code and key generation, data processing, and data statistics. The workflow is as follows: Figure 1 As shown. Assuming each drone holds a location string encoded by the Gray code for that area, the specific process is as follows: Initialization: This algorithm defines a pseudo-random generator. and in the pseudo-random generator A pseudo-random group element is defined in the code to transform a length of... Random string: .

[0035] Gray code and key generation: The three-dimensional space is divided into regions using multi-level octrees, and each region is uniquely encoded using Gray codes. In other words, the encoding of the three-dimensional space divided by the octree can represent... By employing precise spatial partitioning and efficient encoding methods, computational redundancy in distribution statistics is reduced, improving the efficiency and accuracy of 3D spatial statistics. This makes it particularly suitable for complex spatial distribution scenarios involving multiple UAVs.

[0036] Simultaneously, the drone generates a key to protect its location privacy, ensuring that location information is not leaked during subsequent data processing and statistical analysis. Specifically, each UAV uses its location code in three-dimensional space to... Key shares are generated and sent to the server.

[0037] :like Figure 2 As shown, this function is a key algorithm for improving distributed point functions, used to generate distributed keys for two servers. and This ensures the privacy of drone location information while supporting efficient spatial distribution statistics. The algorithm inputs security parameters. Drone location information , This represents the total number of layers into which the three-dimensional space is divided, each... Pseudo-random generator and initialization parameters .

[0038] First, randomly initialize the seeds for both servers. and control bits The subscript "0" corresponds to the first server, and the subscript "1" corresponds to the second server.

[0039] For each layer Through a pseudo-random generator For server Generate path values, :

[0040] Among them, the path is as follows This represents a branch of an octree.

[0041] Secondly, in an octree, each level has eight nodes, so the seed at each level needs to be expanded to obtain the seed and control bits corresponding to each node. Based on the current position information... ,Sure" Path (correct path) and " "Path (incorrect path), for example when..." When, set " "The path is" For each " The path is calculated using the following formula:

[0042] in, and Indicates the correction words on the error path. and These represent the seeds on the erroneous paths in the two servers. and These represent the control bits on the error path in each of the two servers; The correct path is calculated as follows:

[0043] in, This indicates the correct correction words on the correct path.

[0044] Calculate the correction words It is used for consistency correction of statistical results.

[0045] Update current control bits and seed:

[0046] in, Indicates the control bits and seed on the correct path.

[0047] Finally, by accumulating the correction words of all layers and the initial seed Generate key pairs:

[0048] Among them, the last layer of correction words The calculation formula is:

[0049] in, Indicates initialization parameters, This represents the control bit at layer n in server 1.

[0050] In eDPF … It is responsible for "twisting" two octrees of depth n into a unique path (α-path), and It is responsible for "twisting" the two leaves at the end of the path into the final group element. In other words, the first n Managing "where to go", the (n+1)th Control "how much is output in the end".

[0051] Each client will send the key. and Upload it to the corresponding server.

[0052] This step employs an improved distributed point function (eDPF) algorithm: an improved distributed point function algorithm is proposed, which includes correction word generation, key generation, and key evaluation processes, and is combined with an octree structure. By optimizing the tree height and computation method of the distributed point function, the computational complexity is significantly reduced, ensuring the privacy and security of UAV location information during spatial distribution statistics, while improving the efficiency and accuracy of the statistical results.

[0053] Data processing In this step, the server receives a key share from each UAV and uses it. An algorithm is used to aggregate these key shares.

[0054] By entering the server Key and location coding The key is parsed layer by layer and distributed statistical results are calculated by combining the correction words. .

[0055] First, from the key Extracting the initial seed Control bit and the correction words for each layer .

[0056] Then, for each layer Using a pseudo-random generator Based on the seed of the previous layer and correction words Calculate pseudo-random values: Encode according to the current layer position. Select the corresponding path (e.g.) Seed and control bits.

[0057] Finally, through the last layer of seeds and control bits With final correction words Combine and calculate the statistical results: .

[0058] This algorithm ensures the accuracy of distributed statistics and the protection of UAV location privacy through layer-by-layer analysis and correction, while providing efficient three-dimensional spatial statistical capabilities.

[0059] This algorithm ensures the accuracy of distributed statistics and the protection of drone location privacy through layer-by-layer parsing and correction, while providing efficient three-dimensional spatial statistical capabilities. For example, if a drone's region number is 000, after each server processes the data, it calculates the share of 1 at the leaf node corresponding to number 000 in the octree. For instance, server 0's calculation result is -1, and server 1's result is 2.

[0060] Data aggregation In the previous stage, after receiving the aggregated results from the two servers, the requester can obtain the spatial distribution of the drones using the following methods.

[0061]

[0062] This embodiment designs a non-cooperative computing architecture based on two servers, sharing the key with two independent servers that perform data processing and statistical calculations separately. Through this non-cooperative key processing method, even if a single server is attacked, it cannot deduce the drone's specific location information, thus achieving strong protection of the drone's location privacy.

[0063] Furthermore, when the drone's location changes, it needs to update its location information by generating two key-sharing pairs.

[0064] First, the UAV generates a key share to offset the old location and sends it to the server to remove its influence from the statistics of the original location.

[0065] Then, the UAV generates new key shares representing the updated location information and sends these key shares to the server for aggregation. Specifically, the UAV... The algorithm generates an offset key share, and then... Calculate the new key share.

[0066] This invention comprehensively solves the aforementioned technical problems by proposing an eSpat scheme based on octrees and an improved distributed point function (DPF). Through optimized encryption techniques and spatial partitioning algorithms, this scheme not only achieves comprehensive protection of UAV location privacy but also significantly improves the efficiency of spatial distribution statistics.

[0067] First, the location information of drones is fully encrypted when uploaded to the server to prevent it from being inferred or leaked during the statistical process.

[0068] Second, in scenarios involving multiple drones and highly complex three-dimensional spaces, reduce computational and communication overhead and improve statistical response speed.

[0069] Third, design an efficient partitioning and encoding mechanism suitable for three-dimensional space to ensure the accuracy and real-time nature of statistical results.

[0070] This application has significant technical advantages over existing technologies in terms of privacy protection, statistical efficiency, and communication overhead, and is suitable for efficient and privacy-preserving spatial distribution statistics in multi-UAV environments.

[0071] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A privacy-preserving drone distribution statistics method based on an improved distributed point function, characterized in that, The specific process is as follows: Gray code and key generation: The three-dimensional space is divided into regions using a multi-level octree, and each region is uniquely encoded using Gray codes; each UAV generates a key based on its position in the three-dimensional space using an improved distributed point function, and sends it to two corresponding servers; Data processing: The server receives the key from each UAV, and calculates the distributed statistical results of UAV location by parsing the key layer by layer and combining it with the correction words; Data aggregation: The requester receives the data processing results from the server and aggregates them to obtain the spatial distribution of drones.

2. The privacy-preserving UAV distribution statistics method based on an improved distributed point function according to claim 1, characterized in that, The specific process of generating keys by improving distributed point functions is as follows: For each server, a seed and control bits are randomly initialized; a pseudo-random generator is used to generate path values ​​for the corresponding server, where the path values ​​represent branches of an octree; and the correct path and the incorrect path are determined based on the current position information. Calculate the correction words corresponding to the correct path and the incorrect path, update the seed and control bits, and accumulate the correction words of each path in this layer to form the correction words of this layer; A key pair is generated by accumulating the correction words, initial seed, and control bits of all layers.

3. The privacy-preserving UAV distribution statistics method based on an improved distributed point function according to claim 2, characterized in that, The specific process of generating keys by improving distributed point functions is as follows: First, randomly initialize the seeds for both servers. and control bits The subscript "0" corresponds to the first server, and the subscript "1" corresponds to the second server. For each layer Through a pseudo-random generator For server Generate path values, : Among them, the path is as follows Represents a branch of an octree; Secondly, based on the current location information ,Sure" Path and " "Path, i.e., correct path and incorrect path; calculate the correct words for the correct path and incorrect path, and combine the correct words calculated for each path in this layer into the correct words for this layer." ; Finally, by accumulating the correction words of all layers and the initial seed Generate key pairs: 。 4. The privacy-preserving UAV distribution statistics method based on an improved distributed point function according to claim 3, characterized in that, The specific correct words used to calculate the correct and incorrect paths are: For each erroneous path, the corrective term is calculated as follows: in, and This indicates partial correction words on the error path. and These represent the seeds on the erroneous paths in the two servers. and These represent the control bits on the error path in each of the two servers; The corrected term for the correct path is: in, The correct word indicates the correct path.

5. The privacy-preserving UAV distribution statistics method based on an improved distributed point function according to claim 3, characterized in that, The current control bit and seed are updated: in, Indicates the control bits and seed on the correct path.

6. The privacy-preserving UAV distribution statistics method based on an improved distributed point function according to claim 3, characterized in that, The last layer of correction words The calculation formula is: in, Indicates initialization parameters, This represents the control bit at layer n in server 1.

7. The privacy-preserving UAV distribution statistics method based on an improved distributed point function according to claim 3, characterized in that, The specific data processing procedure is as follows: First, from the key Extracting the initial seed Control bit and the correction words for each layer ; Secondly, for each layer Using a pseudo-random generator Based on the seed of the previous layer and correction words Calculate pseudo-random values: Encode according to the current layer position. Select the seed and control bits for the corresponding path; Finally, through the last layer of seeds and control bits With final correction words Combine and calculate the statistical results: .

8. The privacy-preserving UAV distribution statistics method based on an improved distributed point function according to claim 1, characterized in that, The pseudo-random generator and in the pseudo-random generator A pseudo-random group element is defined in the code to transform a length of... Random string: .

9. The privacy-preserving UAV distribution statistics method based on an improved distributed point function according to claim 1, characterized in that, When the drone's location changes, its location information is updated by generating two key-sharing pairs.

10. The privacy-preserving UAV distribution statistics method based on an improved distributed point function according to claim 9, characterized in that, The process of updating its location information by generating two key-sharing pairs is as follows: First, the UAV generates a key share to offset the old location and sends it to the server to remove its influence from the statistics of the original location; Then, the UAV generates new key shares representing the updated location information and sends these key shares to the server for aggregation.

Images