A UAV Terrain Depth Detection Method Based on Multi-Client Function Encryption

Abstract

This invention provides a UAV terrain depth detection method based on multi-client function encryption, mainly including: A. System initialization: In a UAV detection system, there is an aggregation server (Server) and K UAV users. The aggregation server (Server) generates system parameters, and each UAV user interacts to generate key information, etc. B. Sensing data encryption stage: The K UAV users encrypt the measured data using a given function. C. Decryption key share generation stage: Each UAV user randomly generates a random number and calculates it with their key, finally transmitting the result to the aggregation server. D. Decryption key generation aggregation stage: The aggregation server calculates the generated key. E. Decryption stage: The aggregation server (Server) decrypts the obtained ciphertext using the decryption key, finally obtaining the final value by solving the discrete logarithm and sending it to each UAV user. F. Weight update stage: The UAV users calculate according to the given equation and continuously interact with the aggregation server, repeating steps B-F until the data converges, thus obtaining the final result.

Description

Technical Field

[0001] This invention belongs to the field of cryptography and network security, and proposes a method for UAV terrain depth detection based on multi-client function encryption. Background Technology

[0002] With the rapid development of wireless communication technology, the increasing computing and sensing capabilities of mobile devices, and the rapid development of drone technology, drone data collection technology has become a powerful tool for data collection. Its advantages, such as flexible deployment, accurate collection, convenience, speed, and high digitization, have made it a hot topic in the industry and it is used in many fields of urban construction.

[0003] However, the widespread adoption of drone sensing technology is still hindered by many problems. First, there are still many obstacles to drone use, and the noise generated during operation can easily affect the final judgment. Second, drone use generates a lot of sensitive private information; if drones directly transmit this information to aggregation servers, it could lead to privacy leaks and potentially irreversible consequences.

[0004] This noise-contaminated data may cause the final data results provided by the UAV perception system to deviate from reality. Furthermore, the leakage of UAV user privacy may greatly discourage users from participating in UAV swarm intelligence perception. To address these challenges, privacy-preserving truth discovery has become a research hotspot in the field of UAV swarm intelligence perception. Numerous privacy-preserving truth discovery algorithms based on cryptographic techniques such as homomorphic encryption, functional encryption, and secure multi-party computation have been proposed. However, these algorithms still have the following problems:

[0005] First, most existing methods cannot provide complete privacy protection; for example, some algorithms can only protect the privacy of perceived data. Second, even if a very small number of methods can provide comprehensive privacy protection, they have significant computational and communication overhead.

[0006] Third, existing privacy-preserving truth discovery methods based on function encryption are not suitable for multi-task truth discovery scenarios and do not consider the impact of malicious adversaries' active attacks on the system.

[0007] Fourth, many algorithm environments require multiple independent platforms, which is difficult to achieve in practice.

[0008] Fifth, if key information on a trusted third-party platform is leaked, the entire system will be insecure.

[0009] Therefore, in order to solve the above problems, the present invention provides a privacy-preserving truth discovery method based on multi-client distributed function encryption. This method achieves comprehensive privacy protection with low system overhead and can resist active attacks, and only requires a secure platform. Summary of the Invention

[0010] This invention provides a privacy-preserving truth discovery method based on multi-client distributed function encryption. In a user-aware interactive architecture for unmanned aerial vehicles (UAVs), this method utilizes multi-client distributed function encryption to overcome the shortcomings of traditional encryption methods, achieving complete privacy protection with low system overhead and a single server, thereby enhancing system security.

[0011] To achieve the above objectives, the present invention employs the following technical means:

[0012] This invention provides a method for UAV terrain depth detection based on multi-client function encryption, comprising the following steps: A. System initialization: In a UAV perception system, there exists an aggregation server Server and K UAV users p k The aggregation server (Server) generates system parameters based on the security parameter λ. And select two to map to respectively and The hash functions H1 and H2 on the drone publish the public parameters pp = (PG, H1, H2), and each drone user p k generate And generate interactively Make ∑ k∈[K] T k =0,∑ k a k =0 and ∑ k log R k =0. Let ek k =s k ,sk k =(s k ,T k ), where G1 is a cyclic additive group of order p with generator P1, G2 is a cyclic additive group of order p with generator P2, and e is a bilinear mapping.

[0013] B. Sensing Data Encryption Phase: K drone users p k The measured data is encrypted using a given function; C. Key share generation stage: Each drone user p k Generate a random number and calculate it using your key. Finally, send the result to the aggregation server.

[0014] D. Key generation and aggregation stage: After receiving the key shares, the aggregation server will aggregate and calculate to generate a key.

[0015] E. Decryption Phase: The aggregation server decrypts the obtained ciphertext using the decryption key, and finally obtains the final value by solving the discrete logarithm problem, which is then sent to each drone user.

[0016] F. Reliability Information Update Phase: The drone user performs calculations according to the given equations and continuously interacts with the aggregation server, repeating the BE step until the data converges, and then obtains the final result.

[0017] In the above technical solution, step A includes:

[0018] A1. Given the security parameter λ, the number of mobile sensing users K, and the number of sensing tasks M, the aggregation service center Server calculates the system parameters we need.

[0019] A2. Then set two mappings to and H1 and H2 on the hash functions,

[0020] A3. Each drone user will automatically generate an S k And generated through interactive means And publish the public parameters so that ∑ k∈[K] T k =0,∑ k a k =0 and ∑ k log R k =0. The public key is ek. k The private key is (s k T k )

[0021] In the above technical solution, step B includes: different drones first perform a fixed perception task with a label of 'l', such as perceiving terrain depth, etc., and drone user p... k The perceived data is Calculate [u] l ]1 = H1(l); then the ciphertext is output as b is a value known to both the user and the task requester. (Where [c] k The meaning of 1 is multiplication on the elliptic curve 1.

[0022] In the above technical solution, step C includes: including each drone user p k Enter the random number y you prepared. k and the key sk k =(sk T k ), [d k ]2=[y k s k +T k ]2, and return [d k ]2.

[0023] In the above technical solution, step D includes the aggregation server calculating the decryption key [d]2 = Σ after receiving all decryption key shares. k∈[K] [d k ]2 so that it can be used for subsequent calculations.

[0024] In the above technical solution, step E includes: inputting all ciphertext ([c k ]1) k∈[K] ,calculate get And obtain by solving the discrete logarithm And send it to each drone user.

[0025] In the above scheme, step F includes: the drone user performs calculations according to the given equations and continuously interacts with the aggregation server, repeating step BF until the data converges, and then the final result is obtained and the final truth result is returned to the task publisher.

[0026] The UAV perception method based on multi-client distributed function encryption for privacy-preserving truth discovery has the following advantages:

[0027] (1) By using multi-client distributed function encryption and perturbation technology, comprehensive privacy protection can be achieved for perceived data, reliability information and updated truth values.

[0028] (2) By using function encryption technology, the true value result can be obtained directly from the decryption process, avoiding the huge computation and communication overhead brought to the system by homomorphic encryption, obfuscated circuits and other technologies.

[0029] (3) Adopting a unique single-server architecture, based on actual conditions, since the transmission of messages between different servers increases the possibility of key leakage, and since one server can obtain the public information of another server to decrypt private information, it is very difficult for multiple servers to achieve non-collusion. The single-server technology we use makes communication between drone users and aggregation servers more efficient and secure, and eliminates the possibility of multiple servers colluding with each other, which is in line with the actual situation. Attached Figure Description

[0030] Figure 1 This is a flowchart of the present invention.

[0031] Explanation of reference numerals in the attached figures:

[0032] 1. The aggregation center server publishes system parameters and sends information to the drone sensing users;

[0033] 2. The drone senses that the user forwards the encrypted and private key information it receives to the aggregation center server.

[0034] 3- Aggregation center aggregates and generates keys;

[0035] 4. After the aggregation center generates the key, it decrypts it and then sends the decrypted value to the drone's sensing user;

[0036] 5. The drone senses that the user calculates the F1 step and then sends the obtained value to the aggregation server.

[0037] 6. The aggregation server calculates F2 and then sends the obtained value to the drone's sensing user;

[0038] 7. The drone perceives the user's own calculations to implement the F3 step. Detailed Implementation

[0039] The following provides a detailed description of the implementation of this invention. Although this invention will be described and illustrated in conjunction with some specific embodiments, it should be noted that this invention is not limited to these embodiments. On the contrary, any modifications or equivalent substitutions made to this invention should be covered within the scope of the claims of this invention.

[0040] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without these specific details.

[0041] The detailed algorithm flow is as follows:

[0042] Step A: System Initialization

[0043] A1: Aggregation Server (Generated) And select two to map to respectively and The hash functions H1 and H2 on the surface publish the public parameter pp = (PG, H1, H2).

[0044] A2: Each drone user p k generate And generate interactively Make ∑ k∈[K] T k =0, Σ k a k =0 and Σ k logR k=0. Let ek k =s k ,sk k =(s k T k )

[0045] Step B: Perception Report Generation Stage

[0046] B1: For a perception task labeled l, drone user p k The perceived data is Calculate [u] l ]1=H1(l);

[0047] B2: Output ciphertext as b is a value known to both the user and the task requester.

[0048] Step C: Key Share Generation Stage

[0049] Each drone user p k Enter y k and the key dk k =(s k T k ), [d k ]2=[y k s k +T k ]2, and return [d k ]2.

[0050] Step D: Key Generation and Aggregation Phase

[0051] The aggregation server receives all decryption key shares [d k After ]2, aggregate the calculations to decrypt the key [d]2=Σ k∈[k] [d k ]2

[0052] Step E: Decryption key generation stage

[0053] Enter all ciphertext ([c k ]1) k∈[K] ,calculate get And obtain by solving the discrete logarithm And send it to each drone user.

[0054] Step F: Truth update phase

[0055] F1: Drone Sensing User p k calculate Obtain the truth value for this round. Then calculate and Then pk Will Send it to the server.

[0056] F2: After receiving the data, the server calculates:

[0057]

[0058] Calculate for each drone sensing user:

[0059]

[0060] And calculate: ∑ k ω k and ω′ k Send it to the corresponding drone sensing user.

[0061] F3: Drone Sensing User p k Upon receipt, the weights will be updated as follows:

[0062]

[0063] And update Return to step 3 to calculate the decryption key share.

[0064] Repeat the BF phase until convergence, and then output the final truth result. Return it to the task requester.

[0065] In summary, this invention provides a privacy-preserving UAV terrain depth detection method based on multi-client distributed function encryption. The method mainly includes stages such as system initialization, perception data encryption, key share generation, key generation aggregation, decryption, and weight update. The technical effects of each stage are analyzed below:

[0066] System initialization phase: System parameters and hash functions are generated through the aggregation server, and a key is generated for each drone user. The main effect of this phase is to establish a secure communication framework to ensure the security of subsequent data interactions.

[0067] Sensing data encryption phase: Drone users encrypt the measured data using a given function. This step protects the privacy of the original data and prevents unauthorized access during transmission.

[0068] Key share generation phase: Each drone user generates a random number and calculates it with their own key, then sends the result to the aggregation server. This step ensures the security of key distribution and management, while also preparing for the subsequent decryption process.

[0069] Key generation and aggregation phase: The aggregation server aggregates the received key shares to generate the decryption key. This step achieves secure key aggregation, ensuring the accuracy of decryption.

[0070] Decryption phase: The aggregation server uses the generated decryption key to decrypt the ciphertext and obtains the final value by solving the discrete logarithm. This step extracts useful information from the encrypted data while maintaining data privacy.

[0071] Weight update phase: Drone users update the weights based on the decryption results and interact with the aggregation server, repeating the encryption and decryption process until data convergence. This step improves data accuracy and reliability through iterative optimization.

[0072] Compared with the prior art, the main advantages of the present invention include:

[0073] Comprehensive privacy protection: By utilizing multi-client distributed function encryption and perturbation technology, comprehensive privacy protection is achieved for perceived data, reliability information, and updated truth values.

[0074] Computational and communication efficiency: The application of function encryption technology avoids the large amount of computational and communication overhead caused by homomorphic encryption, obfuscated circuits and other technologies.

[0075] Single-server architecture: Adopting a single-server architecture improves the efficiency of communication between drone users and the aggregation server, while reducing the risks of key leakage and collusion between servers in a multi-server architecture.

[0076] In summary, this invention provides an efficient and secure method for unmanned aerial vehicle (UAV) terrain depth detection, effectively solving the problems of privacy protection and data accuracy.

Claims

1. A method for UAV terrain depth detection based on multi-client function encryption, characterized in that, The UAV detection system includes an aggregation server (Server) used to generate initial system values, using p k To represent each drone user, the following steps are included: Step A. System Initialization: Step B. Sensing Data Encryption Phase: K drone users encrypt the relevant data they have measured using a given function; Step C. Key Share Generation Stage: Each drone user randomly generates a random number and calculates it with their key, and finally sends the result to the aggregation server; Step D. Key Generation and Aggregation Phase: The aggregation server aggregates the obtained key shares to obtain the required key; Step E. Decryption stage: The aggregation server decrypts the obtained ciphertext using the decryption key, and finally obtains the final value by solving the discrete logarithm and sends it to each drone user; Step F. Weight Update Phase: The drone user calculates according to the given equation and continuously interacts with the aggregation server, repeating the BE step until the data converges, and the final result is obtained. Step B includes: different drones first pass through a fixed tag for... Perception tasks, drone users p k The perceived data is Calculate [u] l ]1=H1(l); The ciphertext is then output as follows b is a value known to both the user and the task requester, where * T represents the transpose of *, [c k The meaning of 1 is multiplication on the elliptic curve 1; Step C includes each drone user p k Enter the random number y you prepared. k and the key sk k =(s k ,T k ), [d k ]2=[y k s k +T k ]2, and return [d k ]2, where [d k ]2 represents the key share generated by each drone user.

2. The UAV terrain depth detection method based on multi-client function encryption as described in claim 1, characterized in that, Step A includes: A1. Given the security parameter λ, the number of mobile sensing users K, and the number of sensing tasks M, the aggregation server (Server) calculates the system parameters. A2. Then set two mappings to and H1 and H2 on the hash functions, A3. Each drone user will automatically generate an s in PBC. k And generated through interactive means And publish the public parameters so that ∑ k∈[K] T k =0,∑ k a k =0 and ∑ k logR k =0, where the public key is ek k The private key is (s k T k ).

3. The UAV terrain depth detection method based on multi-client function encryption as described in claim 2, characterized in that, Step D includes: After receiving all the key shares, the aggregation server calculates the decryption key [d]2 = ∑ k∈[K] [d k ]2 so that it can be used for subsequent calculations.

4. The UAV terrain depth detection method based on multi-client function encryption as described in claim 3, characterized in that, Step E includes: inputting all ciphertext ([c k ]1) k∈[K] ,calculate get And obtain by solving the discrete logarithm Then it is sent to each drone user, where [α] T This represents an intermediate quantity during the decryption process.

5. The UAV terrain depth detection method based on multi-client function encryption as described in claim 4, characterized in that, Step F includes: The aggregation server sends the calculated values ​​to the drone user; The drone user calculates the weights using a preset function based on the calculated values ​​and sends the results to the aggregation server. The aggregation server receives the calculation results, performs calculation processing, and repeats the above steps until the data converges, thereby obtaining the final data; The aggregation server maintains its ability to receive drone user data throughout the iterative processing.

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