Unmanned aerial vehicle terrain depth detection method based on multi-client function encryption

Abstract

The invention provides an unmanned aerial vehicle terrain depth detection method based on multi-client function encryption, and the method mainly comprises the steps: A, system initialization: in an unmanned aerial vehicle detection system, there are one aggregation server and K unmanned aerial vehicle users, the aggregation server generates system parameters, and the K unmanned aerial vehicle users are used as unmanned aerial vehicle users; each unmanned aerial vehicle user interactively generates information such as a secret key and the like B. In the sensing data encryption stage, K unmanned aerial vehicle users encrypt measured related data through a given function; c, a decryption key share generation stage: each unmanned aerial vehicle user randomly generates a random number, calculates a key of the unmanned aerial vehicle user and the obtained random number, and finally transmits an obtained value to an aggregation server; D, a decryption key generation aggregation stage: the aggregation server can obtain the generated key through calculation; e, a decryption stage: the aggregation server decrypts the obtained ciphertext through a decryption key, and finally obtains a final value through a discrete logarithm solution mode and sends the final value to each unmanned aerial vehicle user; F, a weight updating stage: the unmanned aerial vehicle users carry out calculation according to a given equation and continuously interact with the aggregation server; and the steps B-F are repeated until data convergence, and a final result can be obtained.

Description

Technical Field

[0001] The invention belongs to the field of cryptography and network security, and proposes a terrain depth detection method for an unmanned aerial vehicle based on multi-client function encryption. Background Art

[0002] With the rapid development of wireless communication technology, the increasing computing and perception capabilities of mobile devices and the rapid development of drone technology, drone data collection technology has become a powerful weapon for collecting data. Its flexible deployment, accurate collection, convenience and speed, and high digitization have made it a hot spot in the industry and 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 the use of drones, and it is extremely easy to generate certain noise during use, which ultimately affects the judgment of the results. Secondly, a lot of sensitive privacy information will be generated during the use of drones. If drones directly transmit this information to aggregation servers, it may cause privacy leakage and have irreversible consequences.

[0004] These noise-contaminated data may cause the final data results provided by the drone perception system to deviate from the facts. At the same time, the leakage of drone user privacy is likely to cause drone users to be unwilling to participate in drone swarm perception. In order to meet these challenges, privacy-preserving truth value discovery has become one of the research hotspots in the field of drone swarm perception. A large number of privacy-preserving truth value discovery algorithms based on cryptographic technologies such as homomorphic encryption, functional encryption, and secure multi-party computing have been proposed, but 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 perception data.

[0006] Second, even if there are a few methods that can provide comprehensive privacy protection, they have large computational and communication overheads;

[0007] 3. Existing privacy-preserving truth-value discovery methods based on function encryption cannot adapt to multi-task truth-value discovery scenarios and do not consider the impact of active attacks by malicious adversaries on the system;

[0008] 4. Many algorithmic environments require multiple platforms that do not collude with each other, which is difficult to achieve in practice.

[0009] 5. If the key information on the trusted third-party platform is leaked, the entire system will be unsafe.

[0010] Therefore, in order to solve the above problems, the present invention provides a privacy-preserving truth value discovery method based on multi-client distributed function encryption, which achieves comprehensive privacy protection with low system overhead and can resist active attacks, and only requires a secure platform. Summary of the invention

[0011] The present invention provides a privacy-preserving truth value discovery method based on multi-client distributed function encryption. Under the interactive architecture of drone user perception, the method uses multi-client distributed function encryption to overcome the shortcomings of traditional encryption methods, achieves complete privacy protection with low system overhead and a single server, and thus makes the system secure.

[0012] In order to achieve the above-mentioned purpose, the present invention adopts the following technical means:

[0013] The present invention provides a method for unmanned aerial vehicle terrain depth detection based on multi-client function encryption, comprising the following steps:

[0014] A. System initialization: In a drone perception system, there is an aggregation server Server and K drone users p k , where the aggregation server Server generates system parameters according to the security parameter λ And select two to map to and The hash functions H1 and H2 on the , publish the public parameters pp = (PG, H1, H2), each drone user p k generate and interactively generate So that∑ k∈[K] T k =0,∑ k a k =0 and ∑ k logR k = 0. Let ek k =s k ,sk k =(s k , T k ). Where G1 is a cyclic additive group of order prime number p and generator P1, G2 is a cyclic additive group of order prime number p and generator P2, and e is a bilinear map.

[0015] B. Perception data encryption phase: K drone users p k , encrypt the measured data through a given function;

[0016] C. Key share generation phase: Each drone user p kGenerate a random number randomly and calculate its own key with the obtained random number, and finally pass the obtained value to the aggregation server.

[0017] D. Key generation aggregation phase: After receiving the key shares, the aggregation server aggregates them to generate the key.

[0018] E. Decryption phase: The aggregation server decrypts the ciphertext using the decryption key, and finally obtains the final value by solving the discrete logarithm and sends it to each drone user.

[0019] F. Reliability information update phase: The drone user calculates according to the given equation and continuously interacts with the aggregation server, repeating the BE steps until the data converges to obtain the final result.

[0020] In the above technical solution, step A includes:

[0021] 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

[0022] A2. Then set two mappings to and The hash functions H1 and H2 on

[0023] A3. Each drone user will automatically generate s k , and generate through interactive means And publish the public parameters so that ∑ k∈[K] T k =0,∑ k a k =0 and ∑ k logR k = 0. The public key is ek k , the private key is (s k , T k )

[0024] In the above technical solution, step B includes: different drones first perform a fixed perception task labeled l, such as sensing terrain depth, etc., and the drone user p k The perception 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. k ] l means multiplication on elliptic curve)

[0025] In the above technical solution, 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 returns [d k ]2.

[0026] In the above technical solution, step D includes the aggregation server receiving all the decryption key shares and calculating the decryption key [d]2 = ∑ k∈[K] [d k ]2 for use in subsequent calculations.

[0027] In the above technical solution, step E includes: inputting all ciphertexts ([c k ]1) k∈[n] ,calculate get And solve the discrete logarithm to obtain And sent to each drone user.

[0028] In the above scheme, step F includes: the drone user calculates according to the given equation and continuously interacts with the aggregation server, repeating the BF step until the data converges, and then the final result can be obtained, and the final true value result is returned to the task publisher.

[0029] The privacy-preserving truth value discovery method based on multi-client distributed function encryption for drone perception of the present invention has the following advantages:

[0030] (1) By utilizing multi-client distributed function encryption and perturbation technology, comprehensive privacy protection can be achieved for perception data, reliability information, and updated true values.

[0031] (2) By using functional encryption technology, the true value result can be obtained directly from the decryption process, avoiding the huge computing and communication overhead brought to the system by technologies such as homomorphic encryption and obfuscated circuits.

[0032] (3) A unique single-server architecture is adopted. Based on practical considerations, since the transmission of messages between different servers will increase 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 avoid collusion. The single-server technology we use makes the communication between drone users and the aggregation server more efficient and secure, and eliminates the possibility of collusion between multiple servers, which is in line with the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of the present invention.

[0034] Description of reference numerals:

[0035] 1-The aggregation center Server publishes system parameters and sends information to drone perception users;

[0036] 2-The drone senses the user and forwards the ciphertext and private key information it obtains to the aggregation center server;

[0037] 3-The aggregation center aggregates and generates keys;

[0038] 4- The aggregation center generates the key, decrypts it, and then sends the decrypted value to the drone perception user;

[0039] 5- The drone senses the user and calculates the F1 step by itself and then sends the obtained value to the aggregation server;

[0040] 6-The aggregation server calculates F2 and then sends the obtained value to the drone perception user;

[0041] 7- The drone senses the user and performs calculations to implement step F3. DETAILED DESCRIPTION

[0042] The following will provide a detailed description of the implementation of the present invention. Although the present invention will be described and illustrated in conjunction with some specific embodiments, it should be noted that the present invention is not limited to these embodiments. On the contrary, modifications or equivalent substitutions made to the present invention should all be included in the scope of the claims of the present invention.

[0043] In addition, in order to better illustrate the present invention, numerous specific details are given in the following specific embodiments. It will be understood by those skilled in the art that the present invention can also be implemented without these specific details.

[0044] The detailed algorithm flow is:

[0045] Step A System initialization:

[0046] A1: Aggregation Server Generation And select two to map to and Hash functions H1 and H2 on the same network, publish public parameters pp = (PG, H1, H2)

[0047] A2: Each drone user p k generate and interactively generate So that∑ k∈[K] T k =0,∑ k a k =0 and ∑ k logR k = 0. Let ek k =s k ,sk k =(s k ,T k )

[0048] Step B: Perception report generation phase:

[0049] B1: For a perception task with label l, drone user p k The perception data is Calculate [u l ]1=H1(l);

[0050] B2: Output ciphertext is b is a value known to both the user and the task requester.

[0051] Step C: Key share generation phase:

[0052] Each drone user p k Enter y k , and the key sk k =(s k , T k ), [d k ]2=[y k s k +T k ]2, and returns [d k ]2.

[0053] Step D: Key generation aggregation phase:

[0054] After receiving all the decryption key shares [d k ]2, aggregate and calculate the decryption key [d]2 = ∑ k∈[K] [d k ]2

[0055] Step E function decryption key generation phase:

[0056] Enter all ciphertext ([c k ]1) k∈[n] ,calculate get And solve the discrete logarithm to obtain And sent to each drone user.

[0057] Step F: Truth value update phase:

[0058] F1: UAV perceives user p k calculate Get the true value of this round Then calculate and Then p k Will Pass it to the server.

[0059] F2: After the server receives it, it calculates:

[0060]

[0061] Calculate for each drone-aware user:

[0062]

[0063] And calculate:

[0064] ∑ k ω k and ω k Sent to the corresponding drone perception user.

[0065] F3: UAV perceives user p k After receiving, the updated weight is:

[0066]

[0067] and update Go back to step 3 to calculate the decryption key share.

[0068] Repeat the BF phase until convergence, and use the final true value result Return to the task requester.

[0069] In summary, the present invention provides a privacy-preserving drone terrain depth detection method based on multi-client distributed function encryption. The method mainly includes system initialization, perception data encryption, key share generation, key generation aggregation, decryption and weight update stages. The technical effect analysis of each stage is as follows:

[0070] System initialization phase: Generate system parameters and hash functions through the aggregation server, and generate keys for each drone user. The main effect of this phase is to establish a secure communication framework to ensure the security of subsequent data interaction.

[0071] Perception data encryption stage: The drone user encrypts the measured data through a given function. This step protects the privacy of the original data and prevents unauthorized access to the data during transmission.

[0072] Key share generation phase: Each drone user generates a random number and calculates it with its own key, and transmits the result to the aggregation server. This step ensures the security of key distribution and management, and prepares for the subsequent decryption process.

[0073] Key generation aggregation phase: The aggregation server aggregates the received key shares and generates a decryption key. This step achieves secure key aggregation and ensures the accuracy of decryption.

[0074] 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 enables the extraction of useful information from the encrypted data while maintaining the privacy of the data.

[0075] Weight update phase: The drone user updates the weight according to the decryption result and interacts with the aggregation server, repeating the encryption to decryption process until the data converges. This step improves the accuracy and reliability of the data through iterative optimization.

[0076] Compared with the prior art, the main advantages of the present invention include:

[0077] Comprehensive privacy protection: Utilizing multi-client distributed function encryption and perturbation technology, comprehensive privacy protection of perception data, reliability information, and updated truth values ​​is achieved.

[0078] Computational and communication efficiency: The application of function encryption technology avoids the large amount of computing and communication overhead caused by technologies such as homomorphic encryption and obfuscated circuits.

[0079] Single-server architecture: The single-server architecture improves the efficiency of communication between drone users and aggregation servers, while reducing the risks of key leakage and collusion between servers in a multi-server architecture.

[0080] In summary, the present invention provides an efficient and safe method for UAV terrain depth detection, which effectively solves 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: In a drone detection system, there is an aggregation server Server used to generate the initial value of the system, using p k To represent each drone user, the following steps are included: Step A. System initialization: The aggregation server Server generates system parameters based on the security parameter λ And select two to map to and Hash functions H1 and H2 on the same network, publish public parameters pp = (PG, H1, H2) Each drone user p k generate and interactively generate So that∑ 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 K is the number of drones, and T k , a k , R k ,s k ,ek k is the public key, sk k is the secret key, T in the public key k , a k , R k The meaning is interactively generated data, s k It is data that is randomly generated independently by each drone user; Step B. Perception data encryption phase: K drone users encrypt the measured data through a given function; Step C. Key share generation phase: Each drone user randomly generates a random number and calculates its own key with the obtained random number, and finally transmits the obtained value to the aggregation server; Step D. Key generation aggregation phase: the aggregation server aggregates the obtained key shares to obtain the required key; Step 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 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 steps until the data converges to obtain the final result.

2. A method for unmanned aerial vehicle terrain depth detection based on multi-client function encryption as claimed 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 Hash functions H1 and H2 on A3. Each drone user will automatically generate s k , and generate 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, where the public key is ek k , the private key is (s k , T k ).

3. A method for terrain depth detection of unmanned aerial vehicles based on multi-client function encryption as claimed in claim 2, characterized in that: Step B includes: different drones first pass a fixed perception task with a label l, and drone user p k The perception data is Calculate [u l ]1=H1(l); The ciphertext is then output as b is a value known to both the user and the task requester, where *T represents the transpose of *, [c k ]1 means multiplication on elliptic curve 1.

4. A method for unmanned aerial vehicle terrain depth detection based on multi-client function encryption as claimed in claim 3, characterized in that: 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 returns [d k ]2, where [d k ]2 represents the key share generated by each drone user.

5. A method for detecting terrain depth of an unmanned aerial vehicle based on multi-client function encryption as claimed in claim 4, characterized in that: Step D includes: after receiving all the key shares, the aggregation server Server aggregates and calculates the decryption key [d]2 = ∑ k∈[K] [d k ]2 for use in subsequent calculations.

6. A method for unmanned aerial vehicle terrain depth detection based on multi-client function encryption as claimed in claim 5, characterized in that: Step E includes: inputting all ciphertexts ([c k ]1) k∈[n] ,calculate [d]2), we get And solve the discrete logarithm to obtain Then it is sent to each drone user, where [α] T Represents an intermediate quantity in the decryption process.

7. A method for detecting terrain depth of an unmanned aerial vehicle based on multi-client function encryption as claimed in claim 6, characterized in that: Step F comprises: The aggregation server SERVER sends the solution value to the drone user; The drone user calculates the weight number according to the solution value through a preset function, and sends the calculation result to the aggregation server; The aggregation server receives the calculation result, performs calculation processing, and repeats the above steps until the data converges, thereby obtaining the final data; The aggregation server always maintains the ability to receive drone user data during the iterative processing.

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