CN-121997375-A - Reputation-based privacy protection unmanned aerial vehicle task matching method and system applied to mobile crowd sensing

CN121997375ACN 121997375 ACN121997375 ACN 121997375ACN-121997375-A

Abstract

The invention discloses a reputation-based privacy protection unmanned aerial vehicle task matching method and system applied to mobile crowd sensing, wherein a task requester encrypts a task through entrusting predicate encryption technology, so that only unmanned aerial vehicles meeting attribute conditions can be decrypted; the mobile crowd sensing platform is responsible for task distribution and matching without acquiring attribute information of tasks or unmanned aerial vehicles, the ground station provides a safe data transmission channel among the mobile crowd sensing platform, the trusted mechanism and the unmanned aerial vehicles, and the unmanned aerial vehicles serve as task executors and have the capabilities of decrypting tasks, collecting data and participating in reputation evaluation. According to the invention, a mixed reputation evaluation mechanism is introduced, direct reputation, indirect reputation, role reputation and historical reputation are integrated, comprehensive evaluation and dynamic update of the node trust degree of the unmanned aerial vehicle are realized, the position, identity and reputation privacy of the unmanned aerial vehicle are protected through an encryption technology, efficient and reliable multi-attribute task matching and reputation management are realized on the premise of protecting the privacy of both parties, and the system safety and task execution efficiency are improved.

Inventors

WANG FENGQUN
XU ZHIHAN
ZHANG QINGYANG
JIN HULIN
LI JIAXIN
CUI JIE

Assignees

安徽大学

Dates

Publication Date: 20260508
Application Date: 20260129

Claims (6)

1. A reputation-based privacy protection unmanned aerial vehicle task matching method applied to mobile crowd sensing is characterized by comprising the following steps: step 1, system initialization and key generation, including system initialization, task requester registration and unmanned aerial vehicle registration, wherein trusted authority TA sets a property corpus And generates a system public key System private key Task requester Having a set of attributes And all task attributes The task requester applies for registration with the TA for which the TA generates the delegated predicate encryption key Unmanned aerial vehicle Having a set of attributes And all unmanned aerial vehicle attributes The unmanned aerial vehicle applies for registration to the trusted authority TA, and the TA generates a predicate decryption key for the trusted authority TA Pseudonyms ; Step 2, matching the intelligent sensing tasks of the mobile group; step 2.1, task encryption and release, namely task requester Encryption key using its delegated predicate For task information Encrypting to generate a task ciphertext Submitting the mobile group intelligent sensing platform MCS to the mobile group intelligent sensing platform MCS for broadcasting; Step 2.2, task decryption and confirmation, unmanned aerial vehicle Receiving task ciphertext Thereafter, the key is decrypted using its predicate Decrypting, if the unmanned aerial vehicle meets the task condition, the decrypting is successful, and confirmation information is returned to the mobile crowd sensing platform MCS, and the mobile crowd sensing platform MCS sends recruitment confirmation information to the unmanned aerial vehicle after verifying the confirmation information ; Step 2.3, uploading task data, and successfully receiving the confirmation information Is collecting task data After that, encrypt it as And together with confirmation information The credentials are uploaded to a mobile crowd sensing platform MCS; step 3, evaluating the mixed reputation; step 3.1, performing multi-reference source reputation evaluation, namely performing reputation evaluation among unmanned aerial vehicle nodes based on direct interaction, neighbor recommendation, role information and historical reputation, and generating an unmanned aerial vehicle To unmanned aerial vehicle Individual comprehensive reputation Meanwhile, the MCS of the mobile crowd sensing platform also generates a task feedback report And send to the trusted authority TA; Step 3.2, updating the global reputation, and the trusted authority TA aggregates all individual comprehensive reputations and task feedback to update the unmanned aerial vehicle Global reputation value of (a) And mapped to reputation level ; Step 3.3, updating the secret key and the pseudonym, and generating a new decryption secret key for the unmanned aerial vehicle by the trusted authority TA based on the updated reputation level Pseudonyms 。
2. The reputation-based privacy protection unmanned aerial vehicle task matching method applied to mobile crowd sensing according to claim 1, wherein the system public key in step 1 System private key The generation method of (1) comprises the following steps: Selecting a security parameter And generates a key pair Defining bilinear maps Wherein , , Is of order prime number Is respectively generated into the following cyclic groups Trusted authority TA selects a hash function And randomly select parameters Then calculate the system public key System private key The formula is as follows: , ; The detailed method for the trusted authority TA to generate the delegated predicate encryption key for the task requester is as follows: first, task requester Selecting an asymmetric key pair And And generates a signature ; Wherein, the Is the public key used by the task requester for the signature, Is its corresponding private key; is the public key used by the task requester for encryption, Is its corresponding private key; The task requester then submits its property set SA from which the trusted authority generates Dimension attribute vector Randomly select Calculating a delegated predicate encryption key The formula is as follows: ; The detailed method for generating the predicate decryption key for the unmanned aerial vehicle is as follows: first, unmanned aerial vehicle Selecting a key pair And generates a signature ; Then, the unmanned aerial vehicle submits the attribute set UA thereof, and the trusted authority generates predicate vectors according to the UA Randomly select Calculating decryption keys Randomly selecting ; At the same time, trusted authority random selection Assigning pseudonyms for drones ; Wherein the method comprises the steps of Is unmanned plane Is a true identity of (c).
3. The reputation-based privacy protection unmanned aerial vehicle task matching method applied to mobile crowd sensing according to claim 1, wherein the specific method of mobile crowd sensing task matching in step 2 is as follows: first, task requester Random selection Encryption key using its delegated predicate For extended plaintext Encrypting to generate ciphertext The expression is as follows: ; Wherein, the Is the current timestamp; Task requester will cipher text and timestamp The task broadcasting is carried out by the mobile crowd sensing platform MCS; then, executing task decryption and uploading, wherein the detailed method is as follows: Unmanned plane Decrypting keys using their predicates And (3) calculating: ; If and only if Namely, when the unmanned aerial vehicle attribute meets the task requirement, the unmanned aerial vehicle can correctly decrypt and obtain the task message ; Then, the unmanned aerial vehicle returns confirmation information including pseudonyms to the mobile crowd sensing platform MCS And a task identifier The mobile crowd sensing platform MCS verifies the unmanned aerial vehicle pseudonym and checks the time stamp If the task recruiter is not full, randomly selecting Sending recruitment confirmation information to unmanned aerial vehicle The expression is as follows: ; Successful reception Is collecting task data After that, it is encrypted The expression is: ; Means that the task data is hashed; Then, together with The credentials are uploaded to the mobile crowd sensing platform MCS together.
4. The reputation-based privacy preserving unmanned aerial vehicle task matching method for mobile crowd sensing according to claim 1, wherein the detailed method of step S3 multi-reference source reputation evaluation is as follows: Unmanned plane To another unmanned aerial vehicle Individual comprehensive reputation Comprehensively considering direct reputation of unmanned aerial vehicle nodes Reputation recommendation Role reputation Historical reputation The formula is as follows: ; In the above-mentioned method, the step of, 、、 And Adaptive weights of the corresponding reference sources, respectively, and satisfy ; Wherein the direct reputation Is unmanned plane Unmanned plane and target Directly interacting the obtained reputation value; Unmanned plane Record with unmanned aerial vehicle Each time of interaction And there is one satisfaction for each interaction Put on direct interaction set Wherein , Representation of Thought to be that The message provided is in the wrong way, Representation of For use of The message provided is a message that, Correct, since the new interaction record has higher reference value, a time decay function is introduced as the weight of the direct reputation ; Wherein the method comprises the steps of As the difference between the current time and the recording time, And finally obtaining the direct credit as a control factor: ; Recommendation reputation values Is unmanned plane node By common neighbour nodes Obtained node related to target unmanned aerial vehicle Is an evaluation of (2); to alleviate the influence of the deviation of neighbor opinion, introduce similarity Wherein Recommendation reputation as a weighting factor The calculation is as follows: ; Wherein the method comprises the steps of Is unmanned plane To unmanned aerial vehicle Is a direct reputation of (1), In order to recommend a set of interactions, Is the number of common neighbors; Is unmanned plane node The trust strength related to the current role comprises police unmanned aerial vehicles, commercial unmanned aerial vehicles, private unmanned aerial vehicles and the like; unmanned plane node for target A weighted sum of last historical global reputation values.
5. The reputation-based privacy preserving unmanned aerial vehicle task matching method for mobile crowd sensing according to claim 1, wherein the detailed method of step S3 global reputation evaluation is as follows: The trusted organization combines the comprehensive credit of all individuals of the round And task feedback reporting , Representing the success of the task, Representing a task failure; Unmanned plane Global reputation of (a) The calculation formula of (2) is as follows: ; Wherein, the 、 And Is a feedback weight and satisfies ; Evaluating the set for the node; For individuals to synthesize reputation Sum node To the node Differences between the average composite reputation of (2); A feedback report set; Total number of historical reputation records; Is that A first kth historical global reputation; recording the time interval from the current moment for the kth historical reputation; The trusted authority then maps the global reputation value to a reputation level Calculation of The formula is as follows: ; Updated global reputation level Is further embedded into the decryption vector of the drone, after which the trusted authority is based on the new reputation level Is that Updating a corresponding decryption key Pseudonyms 。
6. A system for implementing the reputation-based privacy-preserving unmanned aerial vehicle task matching method applied to mobile crowd sensing as claimed in any one of claims 1 to 5, comprising a trusted mechanism, a task requester, a mobile crowd sensing platform, a ground station and an unmanned aerial vehicle, wherein the trusted mechanism is configured to perform system initialization, key generation, reputation evaluation and updating, and to assign and update pseudonyms and decryption keys for the unmanned aerial vehicle, the task requester encrypts a task using a delegated encryption key obtained from the trusted mechanism and submits the task ciphertext to the mobile crowd sensing platform, the mobile crowd sensing platform is responsible for receiving, storing and broadcasting the task ciphertext, validating unmanned aerial vehicle task confirmation information, aggregating task data and generating task feedback reports, the ground station provides a secure data transmission channel between the mobile crowd sensing platform, the trusted mechanism and the unmanned aerial vehicle, and the unmanned aerial vehicle receives the task ciphertext from the platform, decrypts and performs tasks using the decryption keys thereof, collects and uploads sensing data, and participates in the multi-reference-based distributed reputation evaluation.

Description

Reputation-based privacy protection unmanned aerial vehicle task matching method and system applied to mobile crowd sensing Technical Field The invention relates to an encryption technology of unmanned aerial vehicle privacy protection, in particular to a reputation-based privacy protection unmanned aerial vehicle task matching method and system applied to mobile crowd sensing. Background Mobile crowd sensing (Mobile Crowdsensing, MCS) utilizes the smart device resources of mass users to cooperatively accomplish complex data acquisition and task execution. Unmanned aerial vehicles have become an important task execution tool in MCS environments with their flexibility, low cost, and rapid deployment capabilities. However, how to evaluate the reliability of the unmanned aerial vehicle in the task coordination process and realize efficient, accurate and privacy-protecting task matching on the basis of the reliability is a key problem to be solved currently. In the aspect of task matching privacy protection, the existing research works focus on hiding the position information of the unmanned aerial vehicle by adopting k-anonymity, differential privacy and other technologies, but generally neglect the confidentiality of the attribute of the task demander, so that the platform can reversely push sensitive information from the matching result, and the situation of unilateral privacy protection is formed. In addition, most schemes do not fully consider the difference of the multi-dimensional attributes of the unmanned aerial vehicle in computing capacity, load capacity, reputation and the like, lack the capability of supporting flexible multi-attribute condition matching, and are difficult to realize intelligent adaptation between tasks and resources. In terms of unmanned plane trust management, although some schemes have introduced reputation mechanisms to promote system security, there are significant limitations. For example, some centralized reputation evaluation methods rely on a single trust center for reputation calculation, and Liu et al propose a non-interactive data reliability evaluation scheme based on a ground control station, and the architecture has a single point of failure risk although a certain degree of privacy protection is achieved through a hash function. In addition, most of the existing scheme evaluation bases are static attributes of the unmanned aerial vehicle, dynamic behaviors and interaction histories of the unmanned aerial vehicle cannot be fully reflected, and performance bottlenecks are easy to form when the node scale is expanded. On the other hand, the distributed reputation mechanism avoids center dependence, but often has single evaluation factor and high communication cost, is easily influenced by local malicious node collusion, and has insufficient evaluation result robustness. More importantly, the existing reputation schemes do not apply privacy protection to reputation values, and an attacker can infer unmanned aerial vehicle behavior tracks by analyzing reputation value change association, so that secondary privacy disclosure is caused. Therefore, the current system design for the unmanned aerial vehicle MCS is urgent to provide an integrated solution for the reputation management mechanism that can give consideration to both the privacy of the task and the performer, support the multi-attribute accurate matching, and have the light weight, the expandability and the privacy protection, so as to realize the safe, reliable and efficient task cooperation in the open dynamic environment. Disclosure of Invention The invention aims to solve the defects in the prior art, and provides a reputation-based privacy protection unmanned aerial vehicle task matching method and system applied to mobile crowd sensing, which can protect the privacy of task demands and unmanned aerial vehicle attributes while realizing multi-attribute condition matching, and realize comprehensive, accurate and privacy-protected reputation management of unmanned aerial vehicle nodes through a hybrid reputation evaluation mechanism, thereby improving the safety, reliability and efficiency of the whole MCS system. The invention discloses a reputation-based privacy protection unmanned aerial vehicle task matching method applied to mobile crowd sensing, which comprises the following steps: step 1, system initialization and key generation, including system initialization, task requester registration and unmanned aerial vehicle registration, wherein trusted authority TA sets a property corpus And generates a system public keySystem private keyTask requesterHaving a set of attributesAnd all task attributesThe task requester applies for registration with the TA for which the TA generates the delegated predicate encryption keyUnmanned aerial vehicleHaving a set of attributesAnd all unmanned aerial vehicle attributesThe unmanned aerial vehicle applies for registration to the trusted authority TA, and the TA generates a