Control method and system, device, and storage medium for mobile crowd sensing

Abstract

The application relates to the technical field of mobile crowd sensing, and discloses a mobile crowd sensing control method, wherein when sensing data is transmitted between participants and a processing platform, the sensing data is encrypted and transmitted by using a public key generated by an RSA encryption algorithm, and the encrypted sensing data needs to be decrypted by using a private key which is not publicly disclosed, so that even if the sensing data is intercepted, the sensing data cannot be decrypted, the reliability of sensing data encryption can be improved, the privacy of a user can be prevented from being leaked in the sensing data transmission process, and the safety of the privacy of the user is ensured. The application further discloses a mobile crowd sensing control device and system and a storage medium.

Description

Technical Field

[0001] This application relates to the field of mobile crowd sensing technology, such as a control method, system, device, and storage medium for mobile crowd sensing. Background Technology

[0002] Mobile Crowdsensing (MCS) is a novel sensing data collection model based on crowdsourcing principles and the sensing capabilities of mobile devices. It typically involves three entities: publishers, participants, and a processing platform. Publishers and participants are generally referred to as users. Publishers are users who need sensing data, responsible for publishing sensing tasks and providing rewards. Participants are users with mobile sensing devices who contribute data acquired using these devices and receive rewards. The processing platform manages MCS activities, such as handling service requests, selecting participants, assigning tasks, processing data, and distributing rewards. However, the data transmission process between participants and the processing platform can easily lead to the leakage of users' privacy information.

[0003] To protect user privacy, related technologies employ encryption algorithms to encrypt data transmission, thereby preventing the leakage of user privacy information. These technologies typically use cryptographic-based encryption algorithms, data perturbation-based encryption algorithms, or anonymity-based encryption algorithms to encrypt data. However, the encryption algorithms in these technologies cannot balance the relationship between privacy protection costs, protection capabilities, and data availability, resulting in relatively low reliability of data encryption.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0006] This application provides a control method, apparatus, device, and storage medium for mobile crowd sensing, which can improve the reliability of sensing data encryption to avoid privacy information leakage.

[0007] In a first aspect, embodiments of this application provide a control method for mobile crowd sensing. This method is applied to a mobile crowd sensing system, which includes a publisher, participants, and a processing platform. The processing platform generates public and private keys using the RSA (Rivest Shamir Adleman) algorithm. The method includes:

[0008] When the processing platform receives the perception task sent by the publisher, it sends the perception task and the public key to the participant.

[0009] The participants collect perception data according to the perception tasks issued by the processing platform;

[0010] The participants use the public key issued by the processing platform to encrypt the sensing data to obtain encrypted sensing data.

[0011] The participant uploads the encrypted perception data to the processing platform;

[0012] The processing platform uses the generated private key to decrypt the encrypted perception data to obtain decrypted perception data;

[0013] The processing platform filters out target perception data that meets preset conditions from the decrypted perception data and sends the target perception data to the publisher.

[0014] Optionally, before issuing the perception task to the participant, the method further includes: the processing platform receiving a virtual identity token sent by the participant; the processing platform verifying the virtual identity token; and after the virtual identity token is verified, the processing platform feeding back virtual identity information to the participant, so that the participant can interact with the processing platform and perform the perception task based on the virtual identity information.

[0015] Optionally, the perception task includes preset data requirements; issuing the perception task to the participants includes: the processing platform selecting multiple target participants from the participants according to the preset data requirements; and the processing platform issuing the perception task to the multiple target participants.

[0016] Optionally, the perception task includes preset data requirements; the participants collect perception data according to the perception task issued by the processing platform, including: multiple target participants collect perception data that meets the preset data requirements.

[0017] Optionally, the sensing task includes preset data requirements; the processing platform filters target sensing data that meets preset conditions from the decrypted sensing data, including: the processing platform filters target sensing data that meets preset data requirements from the decrypted sensing data.

[0018] Optionally, the perception task also includes the perception task reward provided by the publisher; after filtering out the target perception data that meets the preset conditions, the process further includes: the processing platform calculating the quality score of the target perception data; and the processing platform calculating and issuing task rewards to the participants who provided the target perception data based on the quality score of the target perception data and the perception task reward.

[0019] Optionally, the processing platform calculates the quality score of the target perception data using the following formula:

[0020]

[0021] Where QOD is the quality score of the target perception data, n is the number of participants, and x is the number of participants. i x is the contribution value of the i-th participant. m is the average contribution of all participants, and x is the balancing factor.

[0022] Secondly, embodiments of this application provide a mobile crowd sensing system, including a publisher, participants, and a processing platform, wherein the processing platform uses the RSA algorithm to generate a public key and a private key; wherein:

[0023] When the processing platform receives the perception task sent by the publisher, it sends the perception task and the public key to the participant.

[0024] The participants collect perception data according to the perception tasks issued by the processing platform;

[0025] The participants use the public key issued by the processing platform to encrypt the sensing data to obtain encrypted sensing data.

[0026] The participant uploads the encrypted perception data to the processing platform;

[0027] The processing platform uses the generated private key to decrypt the encrypted perception data to obtain decrypted perception data;

[0028] The processing platform filters out target perception data that meets preset conditions from the decrypted perception data and sends the target perception data to the publisher.

[0029] Thirdly, embodiments of this application provide an apparatus for mobile crowd sensing, including a processor and a memory storing program instructions, wherein the processor is configured to execute the control method for mobile crowd sensing as described in the first aspect when running the program instructions.

[0030] Fourthly, embodiments of this application provide a storage medium storing program instructions, characterized in that, when the program instructions are executed, they perform the control method for mobile crowd sensing as described in the first aspect.

[0031] This application provides a control method, apparatus, device, and storage medium for mobile crowd sensing, which can achieve the following technical effects:

[0032] In a mobile crowdsourcing sensing system, the processing platform can generate public and private keys using the RSA encryption algorithm, distribute the public key to participants, and send sensing tasks from the publisher to the participants. After collecting sensing data, participants can encrypt the sensing data using the public key distributed by the processing platform and upload the encrypted sensing data to the processing platform. The processing platform can decrypt the encrypted sensing data using the generated private key to obtain decrypted sensing data, and then feed back the target sensing data that meets preset conditions from the decrypted sensing data to the publisher, thereby enabling the publisher to collect data. In this embodiment, when transmitting sensing data between participants and the processing platform, because the public key generated by the RSA encryption algorithm is used to encrypt the sensing data during transmission, and the encrypted sensing data needs to be decrypted using a private key that is not publicly disclosed, even if the sensing data is intercepted, it cannot be decrypted. This improves the reliability of sensing data encryption, avoids leaking user privacy during sensing data transmission, and ensures user privacy security.

[0033] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0034] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0035] Figure 1 This is a schematic diagram of a mobile crowd sensing method provided in an embodiment of this application;

[0036] Figure 2 This is a schematic diagram of a mobile crowd sensing control method provided in an embodiment of this application;

[0037] Figure 3This is a schematic diagram of an RSA algorithm provided in an embodiment of this application;

[0038] Figure 4 This is a schematic diagram of a function model provided in an embodiment of this application;

[0039] Figure 5 This is a schematic diagram of another function model provided in an embodiment of this application;

[0040] Figure 6 This is a schematic diagram of another function model provided in an embodiment of this application;

[0041] Figure 7 This is a schematic diagram of a sensing data acquisition area provided in an embodiment of this application;

[0042] Figure 8 This is a schematic diagram of a dataset provided in an embodiment of this application;

[0043] Figure 9 This is a schematic diagram of an unencrypted dataset provided in an embodiment of this application;

[0044] Figure 10 This is a schematic diagram of an encrypted dataset provided in an embodiment of this application;

[0045] Figure 11 This is a schematic diagram illustrating a comparison of encryption speeds provided in an embodiment of this application;

[0046] Figure 12 This is a schematic diagram illustrating a comparison of decryption speeds provided in an embodiment of this application;

[0047] Figure 13 This is a schematic diagram illustrating a user quality comparison provided in an embodiment of this application;

[0048] Figure 14 This is a schematic diagram of a control device for mobile crowd sensing provided in an embodiment of this application. Detailed Implementation

[0049] The terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0050] Unless otherwise stated, the term "multiple" means two or more.

[0051] In this embodiment, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0052] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0053] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0054] To provide a more detailed understanding of the features and technical content of the embodiments of this application, the implementation of the embodiments of this application will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this application. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0055] Mobile Crowdsensing (MCS) is a novel sensing data collection model based on crowdsourcing principles and the sensing capabilities of mobile devices. It typically involves three entities: publishers, participants, and a processing platform. Publishers and participants are generally referred to as users. Publishers are users who need sensing data, responsible for publishing sensing tasks and providing rewards. Participants are users with mobile sensing devices who contribute data acquired using these devices and receive rewards. The processing platform manages MCS activities, such as handling service requests, selecting participants, assigning tasks, processing data, and distributing rewards. However, the data transmission process between participants and the processing platform can easily lead to the leakage of users' private information (such as name, gender, home address, social relationships, and location).

[0056] To protect user privacy, related technologies employ encryption algorithms to encrypt data transmission, thereby preventing the leakage of user privacy information. These technologies typically use cryptographic-based encryption algorithms, data perturbation-based encryption algorithms, or anonymity-based encryption algorithms to encrypt data. The encryption algorithms used in these technologies are explained below.

[0057] Cryptographic algorithms are based on cryptography. Cryptography primarily protects data security and privacy through encryption, authentication, and signature techniques. Data is transformed using cryptographic techniques similar to homomorphic encryption and public-key encryption before being uploaded to the task processing platform. For example, symmetric encryption can be used, where the same key is used for both encryption and decryption. Plaintext and the key are transformed together to generate ciphertext, and the same key is used for both encryption and decryption. While this method is fast, the security of the key is difficult to guarantee. Furthermore, while homomorphic encryption can ensure data accuracy and privacy, effectively resist side-channel attacks, and has extremely effective privacy protection, its computational complexity is too high, placing an excessive burden on mobile terminals.

[0058] Encryption algorithms based on data perturbation. Data perturbation refers to protecting sensitive data for privacy before it is submitted to the processing platform using perturbation techniques (such as transformation information technology, noise information technology, etc.) without affecting data usability. This encryption algorithm modifies data without sacrificing its usability and analytical value, thereby achieving the goal of protecting data privacy. Specifically, on the one hand, privacy information can be protected by deploying a local differential privacy framework on IoT devices. That is, before data is transmitted from participants to the processing platform, the original data is masked by random noise, making it difficult for external attackers to obtain the perceived data. On the other hand, after analyzing the multidimensionality and noise uniformity of IoT data, various noises can be added to the wavelet coefficients to balance data privacy and usability. That is, different noise interferences are assigned to each user, and the noise interference corresponding to each data is different. Although the original data uploaded by users can be well hidden, it is difficult to aggregate effective and high-quality data after it is uploaded to the processing platform. Low-quality data will significantly reduce the usability of the data.

[0059] Anonymity-based encryption algorithms. Anonymity-based privacy protection methods are a common privacy protection technology. They primarily involve anonymizing sensitive information while ensuring data availability, thereby reducing the risk of data leakage. Anonymization is achieved by hiding sensitive attributes such as a user's name, gender, phone number, and address, while sharing other attributes. When external attackers want to obtain information, they find it impossible to associate the user's real information with their sensitive information through other means. Anonymity-based privacy protection methods mainly include two types: global anonymity and local anonymity.

[0060] Global anonymization methods involve anonymizing every single data record in a dataset to prevent the relationships between records from being deduced. The most commonly used method is the k-anonymity algorithm, which groups data records with the same sensitive attributes, ensuring each group contains at least k records. Each group is then anonymized, giving all records the same attribute value, thus protecting privacy. However, the k-anonymity algorithm is vulnerable to "information entropy attacks," where attackers can use other attribute information and background knowledge to infer the attribute values ​​of the anonymized records.

[0061] Partial anonymization methods anonymize only sensitive attributes, leaving other attributes unchanged. The most common method is the l-diversity algorithm, which groups data records with the same sensitive attributes and then expands the sensitive attribute values ​​in each group into l distinct values, thus increasing the difficulty for attackers to crack the data. However, the l-diversity algorithm introduces noise and reduces data accuracy.

[0062] Besides k-anonymity and l-diversity algorithms, there are other privacy-preserving methods based on anonymity, such as t-isomorphism and m-differential privacy algorithms. In practical applications, these methods can be selected according to the specific scenario to ensure data accuracy and availability as much as possible while protecting privacy.

[0063] As can be seen from the above, the encryption algorithms in the relevant technologies cannot balance the relationship between privacy protection costs, protection capabilities, and data availability, resulting in low reliability of data encryption.

[0064] Therefore, embodiments of this application provide a control method, system, device, and storage medium for mobile crowd sensing. In these embodiments, when participants transmit sensing data to the processing platform, a public key generated by the RSA encryption algorithm can be used to encrypt the sensing data during transmission. Furthermore, the encrypted sensing data needs to be decrypted using a private key that is not publicly disclosed. This improves the reliability of the sensing data encryption, preventing the leakage of user privacy during the transmission of sensing data and ensuring user privacy security.

[0065] Combination Figure 1As shown in the illustration, this application provides a mobile crowdsourcing sensing system, which includes a publisher, participants, and a processing platform. The publisher can obtain the required data by publishing sensing tasks, which can be sent to the processing platform. Upon receiving a sensing task, the processing platform selects participants based on the preset data requirements of the task. Participants collect sensing data and upload it to the processing platform, which processes and analyzes the submitted data, including data management, data preprocessing, data analysis, quality analysis, and evaluation. Privacy protection is implemented throughout this process to prevent user privacy from being leaked, and rewards and feedback are provided to participants. Thus, the mobile crowdsourcing sensing system realizes multiple functions such as sensing task request, sensing task allocation, sensing data collection, and sensing data analysis.

[0066] It should be noted that in the mobile crowd sensing system of this embodiment, the participants performing the sensing tasks are actually the mobile terminals of the participating users (e.g., mobile phones and wearable devices), not the participating users themselves who control the mobile terminals. Similarly, the publisher sending the sensing tasks is actually the computing device (e.g., a server and a computer) used by the task publisher, not the task publisher itself that controls the computing device.

[0067] Combination Figure 2 As shown, this application provides a control method for mobile crowd sensing, which can be applied to the mobile crowd sensing system described above. In this system, the processing platform generates a public key and a private key using the RSA algorithm. The method includes:

[0068] S21, when the processing platform receives the perception task sent by the publisher, it issues the perception task and public key to the participants.

[0069] S22, participants collect sensory data according to the sensory tasks issued by the processing platform.

[0070] S23, participants use the public key issued by the processing platform to encrypt the sensing data to obtain encrypted sensing data.

[0071] S24, participants upload encrypted perception data to the processing platform.

[0072] S25, the processing platform uses the generated private key to decrypt the encrypted sensing data to obtain the decrypted sensing data.

[0073] S26, the processing platform filters out target perception data that meets preset conditions from the decrypted perception data and sends the target perception data to the publisher.

[0074] In this embodiment, the sensing task where the publisher collects sensing data is handled by a processing platform, and the publisher collects the sensing data from the processing platform. Furthermore, to ensure the publisher collects the required sensing data, data requirements can be preset within the sensing task. These preset data requirements characterize what type of sensing data needs to be collected during the execution of the sensing task.

[0075] The control method for mobile crowd sensing provided in this application embodiment allows the processing platform in a mobile crowd sensing system to generate public and private keys using the RSA encryption algorithm. The public key is then distributed to participants, and sensing tasks from the publisher are sent to them. After collecting sensing data, participants can encrypt the data using the public key distributed by the processing platform and upload the encrypted data to the platform. The processing platform can then decrypt the encrypted sensing data using the generated private key to obtain decrypted sensing data. The platform then feeds back the target sensing data that meets preset conditions from the decrypted sensing data to the publisher, thereby enabling the publisher to collect the data. In this embodiment, when transmitting sensing data between participants and the processing platform, the public key generated by the RSA encryption algorithm is used to encrypt the data. Furthermore, the encrypted sensing data needs to be decrypted using a private key that is not publicly available. Therefore, even if the sensing data is intercepted, it cannot be decrypted, thus improving the reliability of the encrypted sensing data and preventing the leakage of user privacy during data transmission, ensuring user privacy security.

[0076] In step S21 above, before the processing platform issues a perception task to the participant, the participant sends a virtual identity token to the processing platform, which then receives and verifies the virtual identity token. After successful verification, the processing platform provides the participant with virtual identity information, enabling the participant to interact with the processing platform and perform perception tasks based on the virtual identity information.

[0077] In this embodiment, participants need to register their identity with the processing platform before participating in the perception task. To further protect user privacy, participants first send a virtual identity token to the processing platform. After verifying the virtual identity token, the processing platform sends back a signed virtual identity information, PageID, to the participant, replacing the participant's real user identity information. Participants can use PageID to participate in the perception task and interact with the processing platform, making it impossible for the processing platform to know the real user identity information, and preventing attackers from intruding on user privacy through PageID.

[0078] Because a unique PageID needs to be generated for each participant, and this identifier serves to conceal the participant's true identity, this embodiment uses the SHA-256 hash function to encrypt the participant's identity information. A hash function is an algorithm that converts a message of arbitrary length into a fixed-length output. Hash functions are lightweight cryptographic functions that can encrypt a given message in a short time. Hash functions are also one-way; it is difficult to deduce the original information from the encrypted hash function, and the probability of two different values ​​obtaining the same hash function value after hashing is very low. Therefore, using hash functions to encrypt users and achieve user anonymization is relatively effective and secure. In this embodiment, participants are allowed to use a value p, unrelated to their sensitive information, as the input value to the hash function when registering their identity, to obtain their PageID. The specific formula is as follows:

[0079] PageID = Hash(p) (1)

[0080] In formula (1), participants obtain virtual identity information PageID through Hsah(P) and participate in the perception task with virtual identity information.

[0081] In this embodiment, the participant registration process achieves partial anonymization of users. Participants use PageIDs encrypted with a hash function to participate in sensing tasks in the mobile crowdsourcing sensing system, which makes it difficult to associate sensitive information and data information with the participants, thereby protecting their privacy.

[0082] In steps S21 and S22, the processing platform issues perception tasks to the participants, including: the processing platform selecting multiple target participants from the participants according to preset data requirements; the processing platform issuing perception tasks to the multiple target participants; and the participants collecting perception data according to the perception tasks issued by the processing platform, including: the multiple target participants collecting perception data that meets the preset data requirements.

[0083] In this embodiment, to ensure the acquisition of reliable perception data, participants who provide unreliable data can be excluded from the participants, thereby selecting multiple target participants who can provide reliable data to perform the perception task.

[0084] In steps S23 to S25, the core principle of the RSA algorithm is to leverage the principles of large prime numbers and number theory. It ensures encryption security by factoring the product of two large prime numbers, thus increasing the difficulty of factorization. Because this algorithm requires two keys: a public key and a private key, it is also known as "public-key cryptography." That is, data is encrypted using the public key and decrypted using the private key. Figure 3As shown, in the RSA algorithm, the key generation process is as follows: First, select two large prime numbers p and q, calculate their product n = pq, then calculate the Euler's totient function φ(n) = (p - 1) * (q - 1). φ(n) is the value of the Euler's totient function of n, which means the number of positive integers less than n that are relatively prime to n. Then, select an integer e such that 1 < e < φ(n) and e is relatively prime to φ(n). Next, find an integer d such that d * e ≡ 1 (mod φ(n)), that is, the remainder of d * e divided by φ(n) is 1. The purpose of this equation is that only by knowing e and φ(n) can d be obtained for decryption. Since only e and φ(n) are public while d is private. Therefore, e and n form the public key, while p, q, and d form the private key. Then, convert the plaintext into a number and encrypt it using the public key. The encryption process is to encrypt the plaintext m using the public key e to obtain the ciphertext c = m^e mod n. The decryption process is to decrypt the ciphertext c using the private key d to obtain the plaintext m = c^d mod n. Thus, the data encryption and decryption process is completed. The security of the RSA algorithm is based on the confidentiality of the two large prime numbers p and q. As long as p and q are large enough, the RSA algorithm can be guaranteed to be secure. Combined with Figure 3 As shown, taking p = 5 and q = 7 as an example, the process of RSA is illustrated.

[0085] After the publisher publishes the sensing task to the processing platform, the platform generates the public key and private key according to the RSA algorithm. Among them, the private key is saved by the processing platform. The processing platform transmits the sensing task and the public key to the participant. After receiving the task request, the participant encrypts the sensing data to be submitted according to the received public key and sends it to the processing platform. Since the encrypted sensing data can only be decrypted with the private key, even if the sensing data is leaked during the data transmission process, the attacker can only obtain the participant's virtual identity information PageID and the sensing data encrypted by the public key, and cannot decrypt the encrypted sensing data with the private key, thus protecting the privacy of the participant. After receiving the encrypted sensing data submitted by the user, the processing platform decrypts the encrypted sensing data using the private key to obtain the sensing data uploaded by the participant, and evaluates the reputation of the participant using the decrypted sensing data.

[0086] In step S26, the processing platform filters out the target sensing data that meets the preset conditions from the decrypted sensing data, including: The processing platform filters out the target sensing data that meets the preset data requirements from the decrypted sensing data.

[0087] In this embodiment, after the processing platform obtains the perception data uploaded by the participants, it is necessary to screen the perception data according to the criteria of a specific data set. Specifically, the data uploaded by the participants is analyzed based on the numerical time index T and the spatial index L measurement index, and the target perception data that meets the perception task is screened out according to the results, and the low-quality data is excluded. Below, the screening process will be described.

[0088] A standard time interval is defined according to the specific perception data. Set the start time as start_time and the end time as end_time. The effective time for data analysis is [start_time, end_time], and a function model is used to reflect the change of the time index T as the t value changes. In this embodiment, the function Y(x) is used to describe the relationship between the two, and the Y(x) function model is as Figure 4 shown, and the specific formula is as follows:

[0089] Y(x) = sgn(-x) + sgn(x) × σ(-x) × 2 (2)

[0090] In formula (2), sgn(x) is an interval function, and the function model is referenced as Figure 5 shown. Among them, when the independent variable value is greater than 0, the function value is 1; when the independent variable value is equal to 0, the function value is 0; when the independent variable value is less than 0, the function value is -1. By referring to the interval function, it can be intuitively explained that when t is within the interval [start_time, end_time], the data quality is higher, and when it is outside the interval, the data quality is lower.

[0091] Combined with Figure 4 shown, in Y(x), when t < start_time, x is assigned the value of start_time - t. It can be seen that as the t value becomes smaller and smaller, Y(start_time - t) tends to 0; if x is within the effective interval of [start_time, end_time], then x will be assigned the value of t - end_time, and the value of Y(t - end_time) is 1; when t > end_time, the value of Y(t - end_time) will approach 0 as t continues to increase. By referring to the function model, when the t value, that is, the time when the participant collects data is extremely short or extremely long, the time index T will decrease to reflect the quality of the current data. The position index L can also be reflected by the Y(x) function model to reflect the data quality.

[0092] Combined with Figure 5 shown, the (x) function is the Sigmoid function, and the function model is as Figure 6 shown.

[0093] Combined with Figure 7As shown, the cyan area represents region I, the red area represents region II, and the gray area represents region III. Assume the gray area is the target acquisition area, and the red and cyan areas represent the user acquisition areas. Let d represent the distance from the user acquisition area to the target acquisition area. That is, d(I) = 1, d(II) = 2. The specific formulas are as follows:

[0094] Y(d)=sgn(-d)+sgn(d)×σ(-d)×2 (3)

[0095] Combining formula (3), it can be seen that as the distance d increases, the negative impact on the position index L also increases.

[0096] In this embodiment, the perception task also includes a perception task reward provided by the publisher. After step S26, the processing platform calculates the quality score of the target perception data after filtering out the target perception data that meets the preset conditions. Based on the quality score of the target perception data and the perception task reward, the processing platform calculates and distributes the task reward to the participant who provided the target perception data.

[0097] In this embodiment, the solution employs the QOD calculation method within the PACE model to assess data quality. The PACE model is a data quality assessment model that allows for the evaluation and improvement of perceived data quality through participant involvement. The PACE model delegates the data quality assessment and improvement process to data users and data producers to ensure the objectivity and reliability of the assessment results. The PACE model primarily comprises three phases: assessment, evaluation, and control. In the assessment phase, data users and data producers jointly determine key data quality indicators and evaluate the data based on these indicators. The evaluation results are then fed back to the data producers, enabling them to identify any shortcomings in their data. In the control phase, data producers can improve data quality by refining data collection processes and raising data quality control standards.

[0098] Furthermore, the processing platform calculates the quality score of the target perception data using the following formula:

[0099]

[0100] In formula (4), QOD is the quality score of the target perception data, n is the number of participants, and x i x is the contribution value of the i-th participant. mis the average contribution value of all participants, and x is the balancing factor. In formula (4), for each participant i, the absolute value of the difference from the average value of all participants is calculated, and then the reciprocal of the balancing factor x is added. The values ​​calculated by each participant are added together to get the denominator, and the numerator is the value calculated by each participant. The sum is the QOD. Through formula (4), the contribution of each participant to the overall perceived data quality can be calculated based on the contribution value of each participant. Specifically, if a participant's contribution value is close to the average value, then the participant's contribution is small; if it is far from the average value, then the participant's contribution is large. Through formula (4), the contribution of each participant to the overall data quality can be calculated based on the contribution value of each participant and the distance from the average value.

[0101] The process of experimentally verifying the control method of mobile crowd sensing in the above embodiments will be described below.

[0102] In this experiment, based on Python 3.11 and the Windows operating system, the specific experimental settings are shown in Table 1.

[0103] CPU i7-9750H CPU GPU GeForce GTX 1650 ComputingPlatform pycharmcommunityedition2022.3.3 Operating System Windows 10

[0104] Table 1

[0105] Combination Figure 8 As shown, the experimental dataset includes user ID, time, longitude, latitude, and temperature. This dataset collects temperature data for roads in Rome (longitude and latitude 12.45-12.55E, 41.79-41.99N) from 17:00:00 to 20:59:59 on February 3rd. The average temperature in Rome in February is typically between 10 and 16 degrees Celsius. In the experimental evaluation, the precision for temperature, time, and location was set to 0.01 degrees Celsius, 1 second, and 1 meter, respectively.

[0106] This embodiment demonstrates that the control method used for mobile crowd sensing can protect the privacy of users' sensing data, location, and identity. First, the user's ID is encrypted using the one-way hash function SHA256, achieving partial anonymization. The pseudocode for the specific anonymization is as follows:

[0107] Sha256.py

[0108] Input: data.csv

[0109] While True do

[0110] data=read_csv('data.csv')hashed_data=[]

[0111] forrow in data:

[0112] hashed_value=sha256(row['ID'])

[0113] hashed_data.append(hashed_value)

[0114] data['PageID']=hashed_data data=data.drop('ID',axis=1)

[0115] Output:data

[0116] After the participant's virtual identity information, PageID, is generated, the participant will use PageID as their authentication identity for participating in the awareness task. Because the hash function is one-way, external attackers cannot associate the data with sensitive information such as the participant's username or location after obtaining it. Furthermore, the hash function is a lightweight function, which can greatly reduce system overhead.

[0117] For perceived data, this embodiment uses RSA asymmetric encryption to encrypt user data, such as... Figure 9 The image shows the data before encryption, as shown below. Figure 10 The image shows the encrypted dataset. In this embodiment, another asymmetric encryption method, Rabin encryption, is also used to encrypt and decrypt the dataset, and the algorithmic efficiency of the two algorithms is compared by calculation.

[0118] In the comparison, the time required for each algorithm to encrypt and decrypt 500 data entries is calculated, representing the algorithm's efficiency. Both algorithms use a 2048-bit key. Figure 11 and Figure 12 As shown, the RSA algorithm is slightly less efficient than the Rabin algorithm during encryption. During decryption, the efficiency difference between the two algorithms is minimal. This is because the RSA algorithm's key generation process is relatively complex, requiring the generation of two large prime numbers and subsequent prime testing and calculations. The Rabin algorithm, on the other hand, only requires the generation of one large prime number. Furthermore, the RSA algorithm involves multiple multiplications and modulo operations during encryption, resulting in a relatively large computational load. The Rabin algorithm's encryption operation is relatively simple, requiring only square and modulo operations, leading to a smaller computational load. Therefore, the Rabin algorithm has a slight efficiency advantage over the RSA algorithm.

[0119] In terms of privacy protection, the RSA algorithm is more secure than the Rabin algorithm. This is because RSA's security is based on the prime factorization problem of large integers. In RSA, two large prime numbers are chosen as components of the private key, and the public key is the product of these two prime numbers. Breaking the RSA algorithm requires factoring the product of the public key into two prime numbers. Currently, the most effective algorithms are based on large integer factorization, such as the Generalized Sieve of Numbers (GNFS) and the Enumerated Sieve of Primes (ECM). These algorithms have very high computational complexity with large prime numbers, making RSA very difficult to crack. The Rabin algorithm's security is based on the difficulty of the square root problem. The Rabin algorithm uses modular square root operations for encryption and decryption; for a given ciphertext, only knowing its square root can yield the correct plaintext. Square roots are relatively easy to calculate, using methods such as the Tonelli-Shanks algorithm. In contrast, the factorization problem of large integers is much more complex because it requires significantly more computation and time. Therefore, given a sufficiently large key length, the RSA algorithm can provide higher security. In summary, while the RSA encryption algorithm is slightly less efficient, it offers stronger privacy protection than the Rabin algorithm. Therefore, privacy protection methods based on the RSA algorithm can better protect the privacy of task participants without compromising system efficiency.

[0120] In a crowdsourced sensing system, external attackers can steal data and location privacy by eavesdropping on communications between the task platform and task participants. According to the asymmetric encryption method presented in this paper, data is encrypted using a public key issued by the platform before users upload it. Therefore, when an external attacker steals data sent by a task participant, they cannot obtain the participant's data before encryption because they lack the key generated by the platform using the RSA algorithm; thus, user data and location privacy are not leaked. Furthermore, in this scheme, participants participate in the sensing task anonymously after hash encryption. In other words, this method does not reveal the participant's real identity information. Specifically, no adversary other than the participating user can obtain the user's identity information. In summary, this method achieves good results in protecting the identity, data, and location information of participants.

[0121] In the experiment, by visualizing the QOD distribution of participants, participants in the perceptual task were divided into two categories: high-quality users and low-quality users. The incentive mechanism was analyzed by comparing the data quality and rewards received by the two groups. Figure 13As shown, high-quality users consistently achieve high QOD scores in most perceptual tasks. Statistical information indicates that the QOD scores of high-quality users are mostly distributed in the range of (0.6, 0.9), corresponding to rewards mostly distributed between (50, 90). Low-quality users consistently achieve low QOD scores in perceptual tasks, mostly distributed between (0.3, 0.6), and correspondingly, their rewards are also lower, mostly distributed between (30, 60).

[0122] In this embodiment, combined with Figure 13 As shown, comparing the two groups reveals that high-quality users can obtain more rewards by maintaining a higher QOD score, while low-quality users can only obtain fewer rewards, thus demonstrating that the control method for mobile crowd sensing provided in the aforementioned embodiments has a good incentive effect.

[0123] In this embodiment, the RSA asymmetric encryption algorithm is used to encrypt the data. Regarding the incentive mechanism, a reputation mechanism is employed, calculating the data quality of participants before allocating corresponding rewards. In experiments, the RSA encryption algorithm is used to encrypt and decrypt the dataset, and the privacy protection effect of the RSA algorithm is analyzed by comparing the efficiency of the two asymmetric encryption algorithms. The incentive effect of this paper is tested by comparing the rewards for high-quality users and low-quality users. While the RSA asymmetric algorithm can effectively protect privacy compared to symmetric algorithms, it requires more time and computation, placing excessive demands on the system's computation and cost when encrypting large amounts of data. Furthermore, if a higher level of privacy protection is required, excessively long key lengths will significantly impact system efficiency. The incentive mechanism also does not consider the impact on the platform when a large number of low-quality users provide excessive invalid data.

[0124] Combination Figure 14 As shown in the figure, this application provides a control device 140 for mobile crowd sensing, including a processor 100 and a memory 101. Optionally, the device may further include a communication interface 102 and a bus 103. The processor 100, communication interface 102, and memory 101 can communicate with each other via the bus 103. The communication interface 102 can be used for information transmission. The processor 100 can call logical instructions in the memory 101 to execute the control method for mobile crowd sensing described in the above embodiment.

[0125] Furthermore, the logic instructions in the aforementioned memory 101 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0126] The memory 101, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 100 executes functional applications and data processing by running the program instructions / modules stored in the memory 101, thereby implementing the control method for mobile crowd sensing in the above embodiments.

[0127] The memory 101 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 101 may include high-speed random access memory and may also include non-volatile memory.

[0128] This application provides a storage medium storing computer-executable instructions configured to execute the aforementioned control method for mobile crowd sensing.

[0129] The aforementioned storage medium can be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0130] The technical solutions of this application embodiment can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this application embodiment. The aforementioned storage medium can be a non-transitory storage medium, including: USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, and other media capable of storing program code; it can also be a transient storage medium.

[0131] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0132] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0133] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of this application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0134] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A control method for mobile crowd sensing, characterized in that, An application is made in a mobile crowd sensing system, the system comprising a publisher, participants, and a processing platform, wherein the processing platform generates public and private keys using the RSA algorithm; the method includes: When the processing platform receives the perception task sent by the publisher, it sends the perception task and the public key to the participant. The participants collect perception data according to the perception tasks issued by the processing platform; The participants use the public key issued by the processing platform to encrypt the sensing data to obtain encrypted sensing data. The participant uploads the encrypted perception data to the processing platform; The processing platform uses the generated private key to decrypt the encrypted perception data to obtain decrypted perception data; The processing platform filters out target perception data that meets preset conditions from the decrypted perception data and sends the target perception data to the publisher; Before the processing platform issues the perception task to the participant, it also includes: The processing platform receives the virtual identity token sent by the participant; The processing platform verifies the virtual identity token; After the virtual identity token is verified, the processing platform feeds back the virtual identity information to the participant, so that the participant can interact with the processing platform and perform the perception task based on the virtual identity information; The processing platform filters target sensing data that meets preset conditions from the decrypted sensing data, including: The decryption sensing data is analyzed based on time and space indicators; the time and space indicators are used to reflect the quality of the decryption sensing data. Based on the analysis results, target perception data that meets the stated perception task is selected.

2. The method according to claim 1, characterized in that, The perception task includes preset data requirements; The participants are given a perceptual task, including: The processing platform selects multiple target participants from the participants based on the preset data requirements; The processing platform issues the perception task to multiple target participants.

3. The method according to claim 2, characterized in that, The sensing task includes preset data requirements; the participants collect sensing data according to the sensing task issued by the processing platform, including: Multiple target participants collect perceptual data that meets the preset data requirements.

4. The method according to claim 1, characterized in that, The perception task includes preset data requirements; The processing platform filters target sensing data that meets preset conditions from the decrypted sensing data, including: The processing platform filters out target sensing data that meets preset data requirements from the decrypted sensing data.

5. The method according to claim 1 or 4, characterized in that, The perception task also includes the perception task reward provided by the publisher; after filtering out the target perception data that meets the preset conditions, it also includes: The processing platform calculates the quality score of the target perception data; The processing platform calculates and distributes task rewards to the participants who provided the target perception data based on the quality score of the target perception data and the perception task reward.

6. The method according to claim 5, characterized in that, The processing platform calculates the quality score of the target perception data using the following formula: Where QOD is the quality score of the target-perceived data, and n is the number of participants. It is the contribution value of the i-th participant. is the average contribution of all participants, and x is the balancing factor.

7. A mobile crowd sensing system, characterized in that, It includes publishers, participants, and a processing platform, wherein the processing platform uses the RSA algorithm to generate public and private keys; wherein: When the processing platform receives the perception task sent by the publisher, it sends the perception task and the public key to the participant. The participants collect perception data according to the perception tasks issued by the processing platform; The participants use the public key issued by the processing platform to encrypt the sensing data to obtain encrypted sensing data. The participant uploads the encrypted perception data to the processing platform; The processing platform uses the generated private key to decrypt the encrypted perception data to obtain decrypted perception data; The processing platform filters out target perception data that meets preset conditions from the decrypted perception data and sends the target perception data to the publisher; Before the processing platform issues the perception task to the participant, it also includes: The processing platform receives the virtual identity token sent by the participant; The processing platform verifies the virtual identity token; After the virtual identity token is verified, the processing platform feeds back the virtual identity information to the participant, so that the participant can interact with the processing platform and perform the perception task based on the virtual identity information; The processing platform filters target sensing data that meets preset conditions from the decrypted sensing data, including: The decryption sensing data is analyzed based on time and space indicators; the time and space indicators are used to reflect the quality of the decryption sensing data. Based on the analysis results, target perception data that meets the stated perception task is selected.

8. A device for mobile crowd sensing, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to execute, when running the program instructions, the control method for mobile crowd sensing as described in any one of claims 1 to 6.

9. A storage medium storing program instructions, characterized in that, When the program instructions are executed by the processor, they perform the control method for mobile crowd sensing as described in any one of claims 1 to 6.

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