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

Abstract

The invention relates to the technical field of mobile crowd sensing, and discloses a control method for mobile crowd sensing, in the method, when sensing data is transmitted between a participant and a processing platform, the sensing data is encrypted and transmitted by adopting a public key generated by an RSA encryption algorithm, and the encrypted sensing data needs to be decrypted by adopting a private key which is not disclosed to the outside, so that the user experience is improved. Even if the perception data is intercepted, the perception data cannot be decrypted, so that the encryption reliability of the perception data can be improved, the privacy of a user is prevented from being leaked in the transmission process of the perception data, and the privacy security of the user is ensured. The invention further discloses a mobile crowd sensing control device and system and a storage medium.

Description

Technical Field

[0001] The present application relates to the field of mobile crowd intelligence perception technology, for example, to a control method and system, device, and storage medium for mobile crowd intelligence perception. Background Art

[0002] Mobile Crowdsensing (MCS) is a new type of perception data collection model based on crowdsourcing and the perception capabilities of mobile devices. It usually includes three objects: publishers, participants, and processing platforms. Usually, publishers and participants are collectively referred to as users. Publishers are users who need to obtain perception data and are responsible for publishing perception tasks and providing task rewards. Participants are users who hold mobile perception devices and are responsible for contributing the data obtained by using mobile perception devices to obtain task rewards. The processing platform is responsible for the management of MCS activities, such as service request acceptance, participant selection, task allocation, data processing, and reward allocation. Among them, the user's privacy information is easily leaked during the data transmission between participants and the processing platform.

[0003] In order to protect the privacy information of users, encryption algorithms are used in related technologies to encrypt and transmit data, thereby preventing the leakage of users' privacy information. In related technologies, encryption algorithms based on cryptography, encryption algorithms based on data perturbation, or encryption algorithms based on anonymity are usually used to encrypt data. However, the encryption algorithms in related technologies cannot take into account the relationship between privacy protection cost, protection capability, and data availability, so the reliability of data encryption is low.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

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

[0007] In a first aspect, an embodiment of the present application provides a control method for mobile crowd intelligence perception, which is applied to a mobile crowd intelligence perception system, wherein the system includes a publisher, a participant, and a processing platform, wherein the processing platform generates a public key and a private key using an RSA (Rivest Shamir Adleman) algorithm; the method includes:

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

[0009] The participant collects perception data according to the perception task issued by the processing platform;

[0010] The participant encrypts the perception data using the public key issued by the processing platform to obtain encrypted perception 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 selects 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, it also includes: the processing platform receiving the virtual identity token sent by the participant; the processing platform verifying 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 interacts with the processing platform based on the virtual identity information and performs the perception task.

[0015] Optionally, the perception task includes preset data requirements; issuing the perception task to the participants includes: the processing platform screening out 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 participant collects 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 according to the preset data requirements.

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

[0018] Optionally, the perception task also includes a perception task reward provided by the publisher; after screening out the target perception data that meets preset conditions, it also includes: the processing platform calculates the quality score of the target perception data; the processing platform calculates and issues the task reward to the participant who provides 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 i is the contribution value of the ith participant, x m is the average contribution of all participants and x is the balancing factor.

[0022] In a second aspect, an embodiment of the present application provides a mobile crowd intelligence perception system, including a publisher, a participant, and a processing platform, wherein the processing platform generates a public key and a private key using an RSA algorithm; wherein:

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

[0024] The participant collects perception data according to the perception task issued by the processing platform;

[0025] The participant encrypts the perception data using the public key issued by the processing platform to obtain encrypted perception 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 selects target perception data that meets preset conditions from the decrypted perception data, and sends the target perception data to the publisher.

[0029] In a third aspect, an embodiment of the present application provides an apparatus for mobile crowd intelligence perception, comprising a processor and a memory storing program instructions, wherein the processor is configured to execute the control method for mobile crowd intelligence perception as described in the first aspect when running the program instructions.

[0030] In a fourth aspect, an embodiment of the present application provides a storage medium storing program instructions, characterized in that when the program instructions are run, they execute the control method for mobile crowd intelligence perception as described in the first aspect.

[0031] The embodiments of the present application provide a control method, device, equipment, and storage medium for mobile crowd intelligence perception, which can achieve the following technical effects:

[0032] In the mobile crowd intelligence perception system, the processing platform can use the RSA encryption algorithm to generate public keys and private keys, issue the public keys to participants, and send the perception tasks from the publisher to the participants. After the participants collect the perception data, they can use the public key issued by the processing platform to encrypt the perception data, and upload the encrypted perception data to the processing platform. For the encrypted perception data, the processing platform can use the generated private key to decrypt it, obtain the decrypted perception data, and feed back the target perception data that meets the preset conditions in the decrypted perception data to the publisher, thereby realizing the publisher's collection of data. In the embodiment of the present application, when the perception data is transmitted between the participants and the processing platform, the public key generated by the RSA encryption algorithm is used to encrypt the perception data for transmission, and the encrypted perception data needs to be decrypted using a private key that is not disclosed to the public, so even if the perception data is intercepted, it cannot be decrypted, thereby improving the reliability of the encryption of the perception data, so as to avoid leaking the user's privacy during the transmission of the perception data, and ensure the privacy security of the user.

[0033] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0035] Figure 1 is a schematic diagram of mobile crowd intelligence perception provided by an embodiment of the present application;

[0036] Figure 2 is a schematic diagram of a control method for mobile crowd intelligence perception provided by an embodiment of the present application;

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

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

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

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

[0041] Figure 7 is a schematic diagram of a perception data collection area provided in an embodiment of the present application;

[0042] Figure 8 is a schematic diagram of a data set provided in an embodiment of the present application;

[0043] Fig. 9 is a schematic diagram of an unencrypted data set provided in an embodiment of the present application;

[0044] Fig.10 is a schematic diagram of an encrypted data set provided in an embodiment of the present application;

[0045] Fig.11 It is a schematic diagram of encryption speed comparison provided by an embodiment of the present application;

[0046] Fig.12 is a schematic diagram of a decryption speed comparison provided by an embodiment of the present application;

[0047] Fig.13 is a schematic diagram of user quality comparison provided by an embodiment of the present application;

[0048] Fig.14 It is a schematic diagram of a control device for mobile crowd intelligence perception provided by an embodiment of the present application. DETAILED DESCRIPTION

[0049] The terms "first", "second", etc. in the specification, claims, and drawings of the embodiments of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present application described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

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

[0051] In the embodiment of the present application, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0052] The term "and / or" is a description of the association relationship 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" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0054] In order to be able to understand the features and technical contents of the embodiments of the present application in more detail, the implementation of the embodiments of the present application is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present application. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0055] Mobile Crowdsensing (MCS) is a new type of perception data collection model based on crowdsourcing and the perception capabilities of mobile devices. It usually includes three objects: publishers, participants, and processing platforms. Usually, publishers and participants are collectively referred to as users. Publishers are users who need to obtain perception data and are responsible for publishing perception tasks and providing task rewards. Participants are users who hold mobile perception devices and are responsible for contributing the data obtained by using mobile perception devices to obtain task rewards. The processing platform is responsible for the management of MCS activities, such as service request acceptance, participant selection, task allocation, data processing, and reward allocation. Among them, in the process of data transmission between participants and processing platforms, users' privacy information (such as name, gender, home address, social relations, location, etc.) is easily leaked.

[0056] In order to protect the privacy information of users, encryption algorithms are used in related technologies to encrypt and transmit data, thereby preventing the leakage of users' privacy information. In related technologies, encryption algorithms based on cryptography, encryption algorithms based on data perturbation, or encryption algorithms based on anonymity are usually used to encrypt data. The following is an explanation of the encryption algorithms in related technologies.

[0057] Cryptography-based encryption algorithms. Cryptography mainly protects the security and privacy of data through encryption, authentication, signature and other technologies. Cryptographic technologies similar to homomorphic encryption and public key encryption are used to convert data and encrypt it before uploading it to the task processing platform. For example, a symmetric encryption and same-platform encryption method can be used, in which symmetric encryption uses the same key for encryption and decryption, transforms the plaintext and the key together to generate ciphertext, and uses the same key in the encryption and decryption process. Although the encryption and decryption speed is fast, the security of the key is difficult to guarantee. In addition, although the homomorphic encryption method can ensure the accuracy and privacy of the data, accurately resist side channel attacks, and has extremely effective privacy protection functions, the homomorphic encryption password has too much computational complexity, which will bring too much load when running on a mobile terminal.

[0058] Encryption algorithm based on data perturbation. Data perturbation refers to the use of perturbation technology (such as transformation information technology, noise information technology, etc.) to protect the privacy of sensitive data before the data is submitted to the processing platform without affecting the availability of the data. This encryption algorithm modifies the data without losing the practicality and analytical value of the data, thereby achieving the purpose 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 the data is transmitted from the participant to the processing platform, the original data will be shielded according to random noise, making it difficult for external attackers to obtain the perceived data. On the other hand, after analyzing the multidimensionality and noise singularity of IoT data, multiple noises can be added to the wavelet coefficients to balance the privacy and availability of the data. That is, different noise interference is assigned to each user, and the noise interference corresponding to each data is different. Although the original data uploaded by the user can be well hidden, it is difficult to aggregate effective and high-quality data after uploading to the processing platform, and low-quality data will significantly reduce the availability of the data.

[0059] Anonymous encryption algorithm. Anonymous privacy protection method is a common privacy protection technology. It mainly anonymizes sensitive information under the premise of ensuring data availability to reduce the risk of data leakage. Anonymization is achieved by hiding sensitive attributes such as the user's name, gender, phone number, address, etc., and sharing other attributes. When an external attacker wants to obtain information, he finds that he cannot associate the user's real information with the user's sensitive information through other means. There are two main types of anonymity-based privacy protection methods: global anonymity-based and local anonymity-based.

[0060] The method based on global anonymity refers to anonymizing each data record in the data set to ensure that the relationship between data records cannot be inferred. The most commonly used method is the k-anonymity algorithm, which groups data records with the same sensitive attributes to ensure that there are at least k data records in each group, and then anonymizes each group so that the data records in each group have the same attribute value to achieve the purpose of protecting privacy. However, the k-anonymity algorithm may suffer from "information entropy attacks", that is, attackers can use other attribute information and background knowledge to infer the attribute values ​​of anonymized data records.

[0061] Based on the partial anonymity method, only sensitive attributes are anonymized, while other attributes remain the same. The most commonly used method is the l-diversity algorithm, which groups each data record with the same sensitive attribute, and then expands the sensitive attribute values ​​of the data records in each group to l different values, thereby increasing the difficulty for attackers to crack the data. However, the l-diversity algorithm will increase noise and reduce the accuracy of the data.

[0062] In addition to the k-anonymity algorithm and the l-diversity algorithm, there are other privacy protection methods based on anonymity, such as the t-isomorphism algorithm, the m-differential privacy algorithm, etc. In practical applications, the above methods can be selected according to specific scenarios to ensure the accuracy and availability of data as much as possible while protecting privacy.

[0063] It can be seen from the above that the encryption algorithms in related technologies cannot take into account the relationship between privacy protection costs, protection capabilities and data availability, so the reliability of data encryption is low.

[0064] In view of this, the embodiment of the present application provides a control method and system, device, and storage medium for mobile crowd intelligence perception. In the embodiment of the present application, when the perception data is transmitted between the participant and the processing platform, the public key generated by the RSA encryption algorithm can be used to encrypt and transmit the perception data, and the encrypted perception data needs to be decrypted using a private key that is not disclosed to the public. This can improve the reliability of the encryption of the perception data, avoid leaking the user's privacy during the transmission of the perception data, and ensure the privacy security of the user.

[0065] Combination Figure 1As shown, an embodiment of the present application provides a mobile crowd intelligence perception system, which includes a publisher, a participant and a processing platform. Among them, the publisher can obtain the required data by publishing a perception task, and the perception task can be sent to the processing platform. After receiving the perception task, the processing platform selects participants according to the preset data requirements of the perception task. The participants collect the perception data and upload it to the processing platform. The processing platform can process and analyze the perception data submitted by the participants, including data management, data preprocessing, data analysis, quality analysis and evaluation, etc. In this process, privacy protection will be carried out to protect the user's privacy from being leaked, and rewards and feedback will be provided to the participants. In this way, the mobile crowd intelligence perception system realizes multiple functions such as perception task request, perception task allocation, perception data collection and perception data analysis.

[0066] It should be noted that in the mobile crowd-sensing system of this embodiment, the participants who perform the sensing task are actually the mobile terminals of the participating users (such as mobile phones and wearable devices, etc.), not the participating users who control the mobile terminals. The publisher who sends the sensing task is actually the computing device used by the task publisher (such as a server and a computer, etc.), not the task publisher who controls the computing device.

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

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

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

[0070] S23, the participant uses the public key issued by the processing platform to encrypt the perception data to obtain encrypted perception data.

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

[0072] S25, the processing platform uses the generated private key to decrypt the encrypted perception data to obtain decrypted perception 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] Among them, in the embodiment of the present application, the perception task of the publisher to collect perception data is obtained by the processing platform on behalf of the publisher, and the publisher collects the perception data from the processing platform. In addition, in order to ensure that the publisher collects the required perception data, data requirements can be preset in the perception task, and the preset data requirements are used to characterize what kind of perception data needs to be collected during the execution of the perception task.

[0075] Using the control method for mobile crowd intelligence perception provided by the embodiment of the present application, in the mobile crowd intelligence perception system, the processing platform can generate public keys and private keys using the RSA encryption algorithm, issue the public key to the participants, and send the perception task from the publisher to the participants. After the participants collect the perception data, they can use the public key issued by the processing platform to encrypt the perception data, and upload the encrypted perception data to the processing platform. For the encrypted perception data, the processing platform can use the generated private key to decrypt it, obtain the decrypted perception data, and feed back the target perception data that meets the preset conditions in the decrypted perception data to the publisher, thereby realizing the publisher's collection of data. In the embodiment of the present application, when the perception data is transmitted between the participants and the processing platform, the public key generated by the RSA encryption algorithm is used to encrypt the perception data for transmission, and the encrypted perception data needs to be decrypted using a private key that is not disclosed to the public, so even if the perception data is intercepted, it cannot be decrypted, thereby improving the reliability of the encryption of the perception data, so as to avoid leaking the user's privacy during the transmission of the perception data, and ensure the privacy security of the user.

[0076] In the above step S21, before the processing platform issues the perception task to the participant, the participant will send a virtual identity token to the processing platform, and the processing platform will receive 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.

[0077] In this embodiment, participants need to request identity registration from the processing platform before participating in the perception task. In order to further protect the user's privacy information, the participant first sends the virtual identity token to the processing platform. After the processing platform verifies the virtual identity token, it feeds back the signed virtual identity information PageID to the participant, replacing the participant's real user identity information with the virtual identity information. Participants can use PageID to participate in the perception task and interact with the processing platform, so that the processing platform cannot know the real user identity information, and attackers cannot invade the user's privacy from PageID.

[0078] Since it is necessary to generate a unique tag PageID for each participant, and the tag has the function of hiding the true identity of the participant. In this embodiment, the hash function SHA-256 is used to encrypt the identity information of the participants. Among them, the hash function is an algorithm that converts a message of any length into a fixed-length output. The hash function is a lightweight cryptographic function that can encrypt a given message in a short time. The hash function is still one-way, and it is difficult to derive the original information from the encrypted hash function, and the probability that two different values ​​will obtain the same hash function value after hashing is very low. Therefore, it is more effective and safe to use hash functions to encrypt users and realize user anonymization. In this embodiment, participants are allowed to use a value p that is unrelated to their own sensitive information when registering their identities, and use p as the input value of the hash function to obtain their own identity identifier PageID. The specific formula is as follows:

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

[0080] In formula (1), the participant obtains the virtual identity information PageID through Hsah(P) and participates in the perception task with the virtual identity information.

[0081] By adopting this embodiment, the participant registration process achieves the purpose of partial anonymization of users. Participants use PageID encrypted by hash function to participate in perception tasks in the mobile crowd intelligence perception system, which makes it difficult to associate the participants' sensitive information with data information, thereby protecting privacy information.

[0082] In steps S21 and S22, the processing platform issues a perception task to the participants, including: the processing platform screens out multiple target participants from the participants according to preset data requirements. The processing platform issues a perception task to the multiple target participants. The participants collect perception data according to the perception task issued by the processing platform, including: the multiple target participants collect perception data that meets the preset data requirements according to the preset data requirements.

[0083] In this embodiment, in order to ensure that reliable perception data is obtained, participants who provide unreliable data can be excluded from the participants, thereby screening out 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 use large prime numbers and related principles of number theory to ensure the security of encryption by decomposing the factorization difficulty of the number obtained by multiplying two large prime numbers. Since the algorithm requires the use of two keys: a public key and a private key, it is also called "public key cryptography". That is, the public key is used to encrypt data and the private key is used to decrypt data. 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 represents 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 can form the public key, and 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. In this way, 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, illustrate the process of RSA.

[0085] After the publisher publishes the sensing task to the processing platform, the platform generates a public key and a 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 data transmission, the attacker can only obtain the participant's virtual identity information PageID and the sensing data encrypted with 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 according to the results, and the low-quality data is eliminated. Next, 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 function model of Y(x) 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 referred to Figure 5 as 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 in the interval [start_time, end_time], the data quality is relatively high, and when it is outside the interval, the data quality is relatively low.

[0091] Combined with Figure 4 as 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; and 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; while 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 represent the data quality.

[0092] Combined with Figure 5 as 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 is area I, the red area is area II, and the gray area is area III. Assume that the gray area is the target acquisition area, the red area and the cyan area are the areas collected by the user, and d is used to represent the distance from the user collection area to the target collection area. That is, d(I) = 1, d(II) = 2. The specific formula is as follows:

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

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

[0096] In the embodiment of the present application, the perception task also includes the perception task reward provided by the publisher. After step S26, the processing platform calculates the quality score of the target perception data after screening out the target perception data that meets the preset conditions. The processing platform calculates and issues the task reward to the participant who provides the target perception data based on the quality score of the target perception data and the perception task reward.

[0097] In this embodiment, this scheme adopts the QOD calculation method in the PACE model to calculate the quality of the data. The PACE model is a data quality assessment model that can evaluate and improve the quality of perceived data through the participation of participants. The PACE model is to hand over the data quality assessment and improvement process to data users and data generators to ensure the objectivity and reliability of the assessment results. The PACE model mainly includes three stages: assessment, evaluation and control. In the assessment stage, data users and data producers will jointly determine the key indicators of data quality and evaluate the data based on these indicators. In the assessment stage, the assessment results will be fed back to the data generator so that the data producer can understand the shortcomings of the data. In the control stage, the data generator can improve the data quality by improving the data collection process, improving the data quality control standards, etc.

[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 is the contribution value of the ith participant, x mis the average contribution value of all participants, and x is the balance 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 balance factor x is added and the inverse is taken. The values ​​calculated by each participant are added to obtain the denominator, and the numerator is the value calculated by each participant. After adding, it is 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 the contribution value of a participant is close to the average value, then the contribution of the participant is small; if it is far from the average value, then the contribution of the participant 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] Next, the process of experimentally verifying the control method of mobile crowd intelligence perception in the above embodiment will be described.

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

[0103] CPU I7-9750HCPU GPU GeForce GTX1650 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. The dataset collects the temperature of the city roads in Rome (longitude and latitude 12.45-12.55E, 41.79-41.99N) from 17:00:00 to 20:59:59 on February 3. Usually, the average temperature in Rome in February is about 10-16 degrees Celsius. In the experimental evaluation, the accuracy of temperature, time and location is set to 0.01 degrees Celsius, 1 second and 1 meter respectively.

[0106] In this embodiment, the control method for mobile crowd intelligence perception will be demonstrated to protect the user's perception data privacy, location privacy and identity privacy. First, the user's ID is encrypted using the one-way hash function SHA256 to achieve partial anonymization. The specific pseudo code for 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 the identity authentication for participating in the perception task. Since the hash function is one-way, that is, an external attacker cannot associate the data with the participant's user name, location and other sensitive information after obtaining the data. And the hash function is a lightweight function, which can greatly reduce system overhead.

[0117] For the perception data, in this embodiment, RSA asymmetric encryption is used to encrypt the user data, such as Fig. 9 The data before encryption is shown as follows: Fig.10 The encrypted data set is shown in FIG. 1. In this embodiment, another asymmetric encryption method, the Rabin encryption method, is also used to encrypt and decrypt the data set, and the algorithm efficiency of the two algorithms is calculated and compared.

[0118] When comparing, the time required for the two algorithms to encrypt and decrypt 500 pieces of data is calculated to indicate the efficiency of the algorithms. Both algorithms use 2048-bit keys. Fig.11 and Fig.12 As shown in the figure, during the encryption process, the efficiency of the RSA algorithm is slightly lower than that of the Rabin algorithm. During the decryption process, the efficiency of the two algorithms is not much different. Because the key generation process of the RSA algorithm is relatively complex, it is necessary to generate two large prime numbers and perform prime number tests and operations. The Rabin algorithm only needs to generate a large prime number. At the same time, during the encryption operation, the RSA algorithm needs to perform multiple multiplications and modulo operations, and the amount of calculation is relatively large. The encryption operation of the Rabin algorithm is relatively simple, and only square and modulo operations are required, and the amount of calculation is small. Compared with the RSA algorithm, the Rabin algorithm has a slight advantage in efficiency.

[0119] In terms of privacy protection, the RSA algorithm is safer than the Rabin algorithm. This is because the security of the RSA algorithm is based on the prime factorization problem of large integers. In RSA, two large prime numbers are selected as components of the private key, and the public key consists of the product of these two prime numbers. Cracking the RSA algorithm requires decomposing the product in the public key into two prime numbers. The most effective algorithms at present are algorithms based on large integer factorization, such as the generalized number field sieve (GNFS) and the enumerated prime field sieve (ECM). The computational complexity of the above algorithms on large prime numbers is very high, making it very difficult to crack the RSA algorithm. The security of the Rabin algorithm is based on the difficulty of the square root problem. The Rabin algorithm uses modular square root operations for encryption and decryption operations. For a given ciphertext, only by knowing its square root can the correct plaintext be obtained. The square root is relatively easy to solve, and the modular square root can be calculated using methods such as the Tonelli-Shanks algorithm. In contrast, the factorization problem of large integers is much more complicated because factoring large integers requires more computation and time. Therefore, under the premise of using a sufficiently large key length, the RSA algorithm can provide higher security. In summary, the RSA encryption algorithm is slightly inferior in algorithm efficiency, but its privacy protection ability is stronger than the Rabin algorithm. Therefore, the privacy protection method based on the RSA algorithm can better protect the privacy of task participants without affecting the efficiency of the system.

[0120] In the crowd sensing system, external attackers can steal the privacy of data and location by eavesdropping on the communication between the task platform and the task participants. According to the asymmetric encryption method in this paper, the data is encrypted using the public key issued by the platform before the user uploads the data. Therefore, when an external attacker steals the data sent by the task participant, since there is no key generated by the platform using the RSA algorithm, the attacker will not obtain the participant's data before encryption, so the user's data and location privacy will not be leaked. In addition, in this scheme, the participants participate in the sensing task anonymously after hash encryption. In other words, this method does not leak the real identity information of the participants. Specifically, any adversary other than the participating users cannot obtain the user's identity information. In summary, this method has achieved good results in protecting the identity, data and location information of the participants.

[0121] In the experiment, by visualizing the QOD distribution of participants, the participants in the perception task are divided into two categories, namely high-quality users and low-quality users. By comparing the data quality and rewards obtained between the two, the incentive mechanism research in this paper is analyzed. Fig.13As shown in the figure, high-quality users can always generate higher QOD scores in most perception tasks. According to statistical information, the QOD scores of high-quality users are mostly distributed in the range of (0.6, 0.9), and the corresponding rewards they receive are mostly distributed between (50, 90). Low-quality users can always generate lower QOD scores in perception tasks, mostly distributed between (0.3, 0.6), and the corresponding rewards they receive are also lower, mostly distributed between (30, 60).

[0122] In this embodiment, combined Fig.13 As shown, by comparing the two groups, it can be seen that high-quality users can obtain more rewards by maintaining higher QOD scores, while low-quality users can only obtain fewer rewards, which indicates that the control method for mobile crowd intelligence perception provided by the aforementioned embodiment has a good incentive effect.

[0123] In the embodiment of the present application, the data is encrypted using the asymmetric encryption RSA algorithm. In terms of incentive mechanism, a reputation mechanism is adopted. After calculating the data quality of the participants, corresponding rewards are allocated to the participants. In the experiment, the RSA encryption algorithm is used to encrypt and decrypt the data set, 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 article is tested by comparing the rewards for high-quality users and low-quality users. Although the RSA asymmetric algorithm can effectively protect privacy compared to the symmetric algorithm, it also requires more time and calculation, and the calculation and cost requirements of the system are too high when encrypting large amounts of data. At the same time, if a higher level of privacy protection is required, a key length that is too long will greatly affect the efficiency of the system. In terms of incentive mechanism, the impact on the platform when a large number of low-quality users provide too much invalid data is not considered.

[0124] Combination Fig.14 As shown, an embodiment of the present application provides a control device 140 for mobile crowd intelligence perception, including a processor (processor) 100 and a memory (memory) 101. Optionally, the device may also include a communication interface (Communication Interface) 102 and a bus 103. Among them, the processor 100, the communication interface 102, and the memory 101 can communicate with each other through the bus 103. The communication interface 102 can be used for information transmission. The processor 100 can call the logic instructions in the memory 101 to execute the control method for mobile crowd intelligence perception in the above embodiment.

[0125] In addition, the logic instructions in the memory 101 described above may be implemented in the form of software functional units and when sold or used as independent products, may 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 method in the embodiment of the present application. The processor 100 executes functional applications and data processing by running the program instructions / modules stored in the memory 101, that is, implements the control method for mobile crowd intelligence perception in the above embodiment.

[0127] The memory 101 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 101 may include a high-speed random access memory and may also include a non-volatile memory.

[0128] An embodiment of the present application provides a storage medium storing computer executable instructions, wherein the computer executable instructions are configured to execute the above-mentioned control method for mobile crowd intelligence perception.

[0129] The above-mentioned storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0130] The technical solution of the embodiment of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present application. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0131] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0132] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods to implement the described functions for each specific application, but such implementations should not be considered to exceed the scope of the embodiments of the present application. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

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

[0134] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two continuous operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A control method for mobile crowd intelligence perception, characterized in that: Applied to a mobile crowd intelligence perception system, the system includes a publisher, a participant, and a processing platform, wherein the processing platform generates a public key and a private key using an RSA algorithm; the method includes: When the processing platform receives the sensing task sent by the publisher, it sends the sensing task and the public key to the participant; The participant collects perception data according to the perception task issued by the processing platform; The participant encrypts the perception data using the public key issued by the processing platform to obtain encrypted perception 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 selects target perception data that meets preset conditions from the decrypted perception data, and sends the target perception data to the publisher.

2. The method according to claim 1, characterized in that Before issuing the perception task to the participant, the method further 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 virtual identity information to the participant, so that the participant interacts with the processing platform based on the virtual identity information and performs the perception task.

3. The method according to claim 1, characterized in that The sensing task includes preset data requirements; Sending a perception task to the participant includes: The processing platform selects a plurality of target participants from the participants according to the preset data requirements; The processing platform issues the perception task to a plurality of target participants.

4. The method according to claim 3, characterized in that The perception task includes preset data requirements; the participant collects perception data according to the perception task issued by the processing platform, including: The plurality of target participants collect perception data that meets the preset data requirements according to the preset data requirements.

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

6. The method according to claim 1 or 5, characterized in that: The perception task also includes the perception task reward provided by the publisher; after screening out the target perception data that meets the preset conditions, it also includes: The processing platform calculates a quality score of the target perception data; The processing platform calculates and issues a task reward to the participant who provides the target perception data according to the quality score of the target perception data and the perception task reward.

7. The method according to claim 6, 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 perception data, n is the number of participants, and x i is the contribution value of the ith participant, x m is the average contribution of all participants and x is the balancing factor.

8. A mobile crowd intelligence perception system, characterized in that: The invention comprises a publisher, a participant and a processing platform, wherein the processing platform generates a public key and a private key using an RSA algorithm; wherein: When the processing platform receives the sensing task sent by the publisher, it sends the sensing task and the public key to the participant; The participant collects perception data according to the perception task issued by the processing platform; The participant encrypts the perception data using the public key issued by the processing platform to obtain encrypted perception data; The participant uploads the encrypted perception data to the processing platform; The processing platform decrypts the encrypted perception data using the generated private key to obtain decrypted perception data; The processing platform selects target perception data that meets preset conditions from the decrypted perception data, and sends the target perception data to the publisher.

9. A device for mobile crowd intelligence perception, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the control method for mobile crowd sensing as described in any one of claims 1 to 7 when running the program instructions.

10. A storage medium storing program instructions, characterized in that: When the program instructions are run, they execute the control method for mobile crowd intelligence sensing as described in any one of claims 1 to 7.

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