CN113553612B - Privacy protection method based on mobile crowd sensing technology - Google Patents

Abstract

The present invention provides a privacy protection method based on mobile crowd-sensing technology, which includes the steps: before assigning the sensing task, the task issuing end classifies the task receiving end using the spectral clustering algorithm in the sensing layer, and divides the corresponding security Level; the task issuer sorts the perception tasks to be assigned according to the sensitivity, and divides the corresponding security level, and then transmits the perception tasks to the blockchain server of the application layer through the network layer; the blockchain server adopts smart contract technology Restrict the choice of perception tasks at the task receiver. The present invention divides the security level of the task receiving end by using the spectral clustering method, and also divides the security level of the perception tasks to be released, and then adopts the smart contract technology to effectively limit the reception of different levels of perception tasks by the task receiving end permissions, significantly improving system performance and at the same time protecting sensitive information in sensing tasks.

Description

A privacy protection method based on mobile crowd sensing technology

technical field

The invention relates to the field of mobile crowd sensing in the medical internet of things, in particular to a privacy protection method based on mobile crowd sensing technology.

Background technique

The Internet of Medical Things (IoMT) is the application of Internet of Things technology in the field of healthcare. The emergence of the Internet of Medical Things provides a feasible solution to a common problem faced by the traditional medical field, that is, it solves the problem of highly dynamic and distributed How should medical institutions obtain the medical and health information of participating users in a timely and accurate manner. In daily life, IoMT collects the user's medical and health data from various terminal applications distributed in various places, such as tablets, mobile phones, personal computers, smart bracelets, etc., and analyzes the relevant data to make timely analysis of the user's health status. Diagnosis and reporting, this process significantly improves medical efficiency.

Furthermore, in order to more conveniently collect data from terminals, mobile crowd sensing technology (MCS) has attracted widespread attention. MCS technology enables mobile terminal devices in daily life to have the capabilities of perception, computing and communication, and can generate the environment itself. data information. Specifically, according to the type of data provider, MCS can be divided into individual perception and community (group) perception. On the one hand, from the perspective of participating users, it focuses more on the role of personal perception, that is, MCS technology provides users with corresponding health data reports by collecting and calculating personal information data; on the other hand, from the perspective of data collection, it focuses more on The role of community perception, that is, MCS technology provides a large amount of data for hospitals and other medical institutions by gathering information and data of all participating users, so that they can monitor large-scale phenomena, but this also requires more participants to actively provide personal health data. Compared with the traditional medical and health management model, the medical and health management system based on MCS technology has the advantages of effectively saving economic and time costs. However, during the data collection process, the privacy disclosure of user and task data may be involved.

At present, how to effectively protect privacy in the process of data collection has become a research hotspot in mobile crowd sensing technology in IoMT, and some related research results have appeared: Haiming Jin et al. (Incentive mechanism for privacy-aware data aggregation in mobile crowd sensing systems , 2018, 1-14) proposed a brand-new mobile intelligent perception system, which integrates data aggregation, incentives and disturbance mechanisms, in which the disturbance mechanism ensures the user's privacy protection requirements and the accuracy of the disturbed data; Haiqin Wu et al. (Enablingdata trustworthiness and user privacy in mobile crowdsensing, 2019, 2294-2307) proposed a trusted and privacy-protected mobile crowdsensing strategy that does not require a trusted third party. In addition to anonymous identity verification, a protocol based on blind signature is also designed to realize anonymous authorization verification of users; Jinwen Xi et al. (CrowdBLPS: Ablockchain-based location privacy-preserving mobile crowdsensing system, 2019, 16(6)) introduced the idea of smart contracts, A two-stage method is proposed, including the pre-registration stage and the final selection stage, to achieve the purpose of protecting location privacy; Jianbing Ni et al. (Enablingstrong privacy preservation and accurate task allocation for mobilecrowdsensing,2020,19(6):1317-1331) A mobile crowd sensing system with strong privacy protection and user credit management is proposed, which uses proxy re-encryption and BBS+signature technology to prevent user privacy from leaking.

However, most of the existing methods focus on protecting the privacy of users’ personal sensitive data, and the protection of sensitive information in sensing tasks still needs to be strengthened; The method of not being leaked has become a technical problem to be solved urgently by those skilled in the art.

Contents of the invention

The technical problem to be solved by the present invention is to provide a privacy protection method based on mobile crowd sensing technology, which can effectively prevent sensitive information in sensing tasks from being maliciously stolen.

In order to solve the above technical problems, the technical solution adopted in the present invention is: a privacy protection method based on mobile crowd sensing technology, comprising steps:

S1. Before assigning sensing tasks, the task issuing end first uses the spectral clustering algorithm to classify the task receiving end in the sensing layer, and divides each classification cluster into a corresponding security level according to the degree of influence after classification;

S2. The task issuing end sorts the sensing tasks to be assigned according to sensitivity, and divides the corresponding security levels for the sensing tasks in order, and then transmits the sensing tasks to the area of the application layer through the network layer In the block chain server;

S3. The block chain server uses smart contract technology to limit the selection of the sensing task by the task receiving end.

The beneficial effect of the present invention is that: the present invention provides a privacy protection method based on mobile crowd sensing technology, before the distribution and reception of sensing tasks, the task issuer uses the spectral clustering method to perform security level assessment on the task receiver According to the sensitivity ranking of the sensitive information in the sensing task, the sensing task is also divided into security levels, and then combined with the smart contract technology in the blockchain server, it can effectively limit the task receiver's ability to perceive different levels of sensing tasks. Receive permissions, significantly improve system performance and protect sensitive information in perception tasks.

Description of drawings

1 is an overall flowchart of a privacy protection method based on mobile crowd sensing technology according to an embodiment of the present invention;

FIG. 2 is a schematic framework diagram of a privacy protection method based on mobile crowd sensing technology according to an embodiment of the present invention;

FIG. 3 is a specific flowchart of a privacy protection method based on mobile crowd sensing technology according to an embodiment of the present invention.

Detailed ways

In order to describe the technical content, achieved goals and effects of the present invention in detail, the following descriptions will be made in conjunction with the embodiments and accompanying drawings.

Please refer to Figure 1 to Figure 3, a privacy protection method based on mobile crowd sensing technology, including steps:

S1. Before assigning sensing tasks, the task issuing end first uses the spectral clustering algorithm to classify the task receiving end in the sensing layer, and divides each classification cluster into a corresponding security level according to the degree of influence after classification;

S2. The task issuing end sorts the sensing tasks to be assigned according to sensitivity, and divides the corresponding security levels for the sensing tasks in order, and then transmits the sensing tasks to the area of the application layer through the network layer In the block chain server;

S3. The block chain server uses smart contract technology to limit the selection of the sensing task by the task receiving end.

From the above description, it can be known that the beneficial effect of the present invention lies in: before the assignment and reception of the sensing task, the task issuing end uses the spectral clustering method to divide the security level of the task receiving end, and according to the sensitivity of the sensitive information in the sensing task Degree sorting divides the perception tasks into security levels, and then combines it with the smart contract technology in the blockchain server, which can effectively limit the task receiving end's receiving authority for different levels of perception tasks, significantly improving system performance and at the same time improving the perception Sensitive information in missions is protected.

Further, the spectral clustering algorithm is based on four indicators: node degree centrality, node betweenness centrality, node local clustering coefficient and node degree-based graph entropy.

Further, in the step S1, the classification of the task receiving end using the spectral clustering algorithm in the perception layer specifically includes the following steps:

S10. Composing the task receiving end into a node in a social network, and constructing a similarity graph based on the four indicators as a reference for calculating node similarity;

S11. Obtain the vector representation of each node in the social network according to formula (1):

x _i ＝<D(v _i ),BC(v _i ),LC(v _i ),I _f (G(v _i ))>(1);

Among them, D(v _i ) is the degree centrality of the node, BC(v _i ) is the betweenness centrality of the node, LC(v _i ) is the local clustering coefficient of the node, _If (G(v _i ) ) is the degree-based graph entropy of the node;

S12, after obtaining the vector representation of each node, calculate the similarity between two nodes v _i and v _j according to formula (2):

Select the top K nodes most similar to v _i as the neighbor nodes of v _i , and add corresponding edges to form a similarity graph G ^s ;

S13, calculate the Laplacian matrix according to formula (3):

L=D-W (3);

Among them, D is the degree matrix, W is the adjacency matrix;

In the adjacency matrix, the weight between every two nodes is calculated as follows:

ω _ij =f _sim (v _i ,v _j ) (4);

S14. According to the formula (5), use the ratio cutting method to balance the number of nodes in each subset:

S15, forming a feature matrix Y according to the T feature vectors in the Laplacian matrix;

S16. Input the feature matrix Y into the k-means algorithm for clustering, and the clustering result is each user subgroup C _i ;

S17. Obtain the influence of each of the user subgroups according to formula (6):

in, is the average of the degree centrality of the nodes mentioned in C _i , /> is the average betweenness centrality of nodes mentioned in C _i , /> is the average value of the local clustering coefficient of the nodes in C _i , is the average value of the degree-based graph entropy of the nodes in C _i .

Further, the step S11 also includes:

The calculation of the betweenness centrality of the nodes is as follows:

Further, the step S11 also includes:

The calculation of the node local clustering coefficient is as follows:

Among them, μG(v _i ) and ωG(v _i ) respectively represent the number of triangles and triangles in G(v _i ).

Further, the step S11 also includes:

The degree-based graph entropy of the nodes is calculated as follows:

If α=1, then:

Among them, n is the number of nodes in the graph, and m is the number of edges in the graph.

It can be seen from the above description that in the social network composed of task receivers, according to the four node indicators, the task receivers are divided into security levels through the spectral clustering method, which effectively reduces the risk of collusion attacks.

Further, the step S2 also includes:

The task issuing end divides the perception task to be assigned into a plurality of different subtasks.

From the above description, it can be known that dividing the perception task into different subtasks can restrict the task receiver from stealing the sensitive information of the complete perception task, thereby preventing the privacy leakage in the perception task.

Further, in the step S2, the task issuer sorts the sensing tasks to be assigned according to sensitivity, and divides the corresponding security levels for the sensing tasks in order, specifically:

The task issuing end sorts the multiple subtasks according to the degree of sensitivity of the sensitive information in the subtasks from large to small, and sequentially divides the subtasks into different subtasks according to the number of preset levels. security level.

It can be seen from the above description that after the sub-tasks of the perception task are divided into security levels, the collusion attack can be effectively avoided in combination with the task receiving end that has also been divided into security levels.

Further, the step S3 also includes:

The smart contract is an electronic contract in the hyperledger, and the rules, supervision and decision-making of the electronic contract are preset by the blockchain server.

From the above description, it can be seen that through the preset rules, supervision and decision-making of the electronic contract, it can effectively prevent the task receiver from mastering the sensitive information in the entire sensing task and prevent the privacy from collusion attacks.

Further, the step S3 is specifically:

When the task receiver requests access to the perception task in the blockchain server, the electronic contract detects whether the security level of the task receiver is the same as the selected security level of the perception task, and if they are the same Then the task receiving end can receive the selected sensing task, otherwise, the task receiving end cannot receive the selected sensing task.

From the above description, it can be seen that the access control to the task receiver is completed through the smart contract, which can effectively protect the sensitive information in the perception task from being stolen by the malicious task receiver.

Please refer to Fig. 1, embodiment one of the present invention is:

A privacy protection method based on mobile crowd-sensing technology. This embodiment is based on the background of the medical Internet of Things. The task issuing end at the perception layer is corresponding to the task receiving end, that is, the participating users and the medical sensing tasks that need to be issued. After the security level classification, the medical perception task is submitted to the blockchain server in the application layer through the network layer for storage, and the task receiving end of the perception layer can select the task, and at the same time, the smart contract technology is used in the blockchain server. Complete the access control permissions on the receiving end of the task.

As shown in Figure 1, a kind of privacy protection method based on mobile crowd sensing technology of the present embodiment comprises steps:

S1. Before assigning sensing tasks, the task issuing end first uses the spectral clustering algorithm to classify the task receiving end in the sensing layer, and divides each classification cluster into a corresponding security level according to the degree of influence after classification.

S2. The task issuer sorts the sensing tasks to be assigned according to the sensitivity, and divides the corresponding security levels for the sensing tasks in order, and then transmits the sensing tasks to the blockchain server of the application layer through the network layer.

S3. The blockchain server uses smart contract technology to limit the choice of the task receiver to the sensing task.

Here, it should be noted that there is no strict time sequence restriction between step S1 and step S2 in this embodiment. For example, it may be that the task issuing end first divides the security level of the sensing task, and then divides the security level of the task receiving end; it may also be that the task issuing end first divides the security level of the task receiving end, and then divides the security level of the sensing task; or it may At the same time, the security level is divided for the sensing task and the task receiving end.

That is to say, in this embodiment, before the distribution and reception of perception tasks, the task issuing end uses the spectral clustering method to divide the security level of the task receiving end, and then combines it with the smart contract technology in the blockchain server, It can effectively limit the receiving authority of the task receiving end to different levels of sensing tasks, significantly improve system performance and protect sensitive information in sensing tasks.

Please refer to Fig. 1 to Fig. 3, embodiment two of the present invention is:

On the basis of the first embodiment above, in a privacy protection method based on mobile crowd sensing technology in this embodiment, the spectral clustering algorithm is based on four indicators: node degree centrality, node betweenness centrality, and node local clustering Degree-based graph entropy for coefficients and nodes.

Wherein, in order to construct the similarity graph, in step S1 of the above-mentioned embodiment 1, in the perception layer, the spectral clustering algorithm is used to classify the task receiving end, which specifically includes the following steps:

S10. Form the task receiving end into nodes in the social network, and construct a similarity graph based on the four indicators as references for calculating node similarity.

S11. Obtain the vector representation of each node in the social network according to formula (1):

x _i ＝<D(v _i ),BC(v _i ),LC(v _i ),I _f (G(v _i ))>(1);

Among them, D(v _i ) is node degree centrality, BC(v _i ) is node betweenness centrality, LC(v _i ) is node local clustering coefficient, _If (G(v _i )) is node degree-based Graph entropy.

In this embodiment, the calculation of node betweenness centrality is as follows:

The calculation of node local clustering coefficient is as follows:

Among them, μG(v _i ) and ωG(v _i ) respectively represent the number of triangles and triangles in G(v _i ).

The degree-based graph entropy of a node is calculated as follows:

If α=1, then:

Among them, n is the number of nodes in the graph, and m is the number of edges in the graph.

S12. After obtaining the vector representation of each node, calculate the similarity between the two nodes vi and vj according to formula (2):

Select the top K nodes most similar to v _i as neighbor nodes of v _i , and add corresponding edges to form similarity graph G ^s .

S13, calculate the Laplacian matrix according to formula (3):

L=D-W (3);

Among them, D is the degree matrix, W is the adjacency matrix;

In the adjacency matrix, the weight between every two nodes is calculated as follows:

ω _ij =f _sim (v _i ,v _j ) (4).

S14. According to the formula (5), use the ratio cutting method to balance the number of nodes in each subset:

S15. Form a feature matrix Y according to the T feature vectors in the Laplacian matrix.

S16. Input the feature matrix Y into the k-means algorithm for clustering, and the clustering result is each user subgroup C _i .

S17. According to the formula (6), the influence of each user subgroup is obtained:

in, is the average of degree centrality of nodes in C _i , /> is the average betweenness centrality of nodes in C _i , /> is the average value of the local clustering coefficient of nodes in C _i , /> is the average degree-based graph entropy of nodes in C _i .

That is to say, in this embodiment, in the social network composed of task receivers, the task receivers are divided into security levels by spectrum clustering method according to four node indicators, which effectively reduces the risk of collusion attacks.

Wherein, in this embodiment, step S2 also includes:

The task issuer divides the perception task to be assigned into multiple subtasks.

That is, in this embodiment, dividing the sensing task into different sub-tasks can restrict the task receiving end from stealing the sensitive information of the complete sensing task, thereby preventing privacy leakage in the sensing task.

Then in step S2 of the first embodiment above, the task issuer sorts the sensing tasks to be assigned according to the sensitivity, and divides the corresponding security levels for the sensing tasks in order, specifically as follows:

The task issuer sorts the multiple subtasks according to the degree of sensitivity of the sensitive information in the subtasks from large to small, and sequentially divides the subtasks into different security levels according to the preset number of levels.

That is, in this embodiment, after all the subtasks of the perception task are divided into security levels, the collusion attack can be effectively avoided in combination with the task receiving end that has also been divided into security levels.

Wherein, step S3 also includes:

A smart contract is an electronic contract in the hyperledger. The rules, supervision and decision-making of the electronic contract are preset by the blockchain server. In this embodiment, through the preset rules, supervision and decision-making of the electronic contract, the system itself completes some rules, supervision and decision-making in the process of applying for access control perception tasks to the task receiver, which can effectively prevent the task receiver from mastering the entire perception process. Sensitive information in missions and protecting privacy from collusive attacks.

Then the step S3 of the above-mentioned embodiment 1 is specifically:

When the task receiving end requests to access the sensing task in the blockchain server, the electronic contract checks whether the security level of the task receiving end is the same as the security level of the selected sensing task. If they are the same, the task receiving end can receive the selected sensing task. Otherwise, the task receiver cannot receive the selected sensing task.

That is, in this embodiment, the access control to the task receiving end is completed through the smart contract, which can effectively protect the sensitive information in the sensing task from being stolen by the malicious task receiving end.

In summary, a privacy protection method based on mobile crowd sensing technology provided by the present invention has the following beneficial effects:

(1) Analysis from the degree of privacy protection: the present invention is applied to the field of mobile crowd sensing under the background of the medical Internet of Things. During the process of uploading and receiving medical sensing tasks, sensitive information contained in the sensing tasks may be maliciously leaked Therefore, in the present invention, the spectral clustering method is used to classify the task receiving end, and at the same time, the perception task is divided into different sub-tasks, and the sub-tasks are also divided into corresponding security levels according to their sensitivity. According to the smart contract in the chain server, the task receiving end can only receive subtasks with the same security level as itself, which can prevent a malicious task receiving end from fully grasping the sensitive information of a sensing task, so as to effectively protect the tasks issued by the task issuing end. privacy of medical perception tasks;

(2) Analysis of system performance: the system used in the present invention can still have high throughput and low delay even when the task receiving end and perception tasks are classified, and can maintain a better system performance.

The above description is only an embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent transformations made by using the description of the present invention and the contents of the accompanying drawings, or directly or indirectly used in related technical fields, are all included in the same principle. Within the scope of patent protection of the present invention.

Claims (4)

1. A privacy protection method based on mobile crowd sensing technology, characterized in that, comprising steps: S1. Before assigning sensing tasks, the task issuing end first uses the spectral clustering algorithm to classify the task receiving end in the sensing layer, and divides each classification cluster into a corresponding security level according to the degree of influence after classification; The spectral clustering algorithm is based on four indicators: node degree centrality, node betweenness centrality, node local clustering coefficient and node degree-based graph entropy; In the step S1, the classification of the task receiving end using the spectral clustering algorithm in the perception layer specifically includes the following steps: S10. Composing the task receiving end into a node in a social network, and constructing a similarity graph based on the four indicators as a reference for calculating node similarity; S11. Obtain the vector representation of each node in the social network according to formula (1): x _i ＝<D(v _i ),BC(v _i ),LC(v _i ),I _f (G(v _i ))>(1); Among them, D(v _i ) is the degree centrality of the node, BC(v _i ) is the betweenness centrality of the node, LC(v _i ) is the local clustering coefficient of the node, _If (G(v _i ) ) is the degree-based graph entropy of the node; S12, after obtaining the vector representation of each node, calculate the similarity between two nodes v _i and v _j according to formula (2): Select the top K nodes most similar to v _i as the neighbor nodes of v _i , and add corresponding edges to form a similarity graph G ^s ; S13, calculate the Laplacian matrix according to formula (3): L=D-W (3); Among them, D is the degree matrix, W is the adjacency matrix; In the adjacency matrix, the weight between every two nodes is calculated as follows: ω _ij =f _sim (v _i ,v _j ) (4); S14. According to the formula (5), use the ratio cutting method to balance the number of nodes in each subset: S15, forming a feature matrix Y according to the T feature vectors in the Laplacian matrix; S16. Input the feature matrix Y into the k-means algorithm for clustering, and the clustering result is each user subgroup C _i ; S17. Obtain the influence of each of the user subgroups according to formula (6): in, is the average of the degree centrality of the nodes mentioned in C _i , /> is the average betweenness centrality of nodes mentioned in C _i , /> is the average value of the local clustering coefficient of the node in C _i , /> is the average value of degree-based graph entropy of nodes described in C _i ; S2. The task issuing end sorts the sensing tasks to be assigned according to sensitivity, and divides the corresponding security levels for the sensing tasks in order, and then transmits the sensing tasks to the area of the application layer through the network layer In the block chain server; Said step S2 also includes: The task issuing end divides the perception task to be assigned into a plurality of different subtasks; In the step S2, the task issuing end sorts the sensing tasks to be assigned according to sensitivity, and divides the corresponding security levels for the sensing tasks in order, specifically: The task issuing end sorts the multiple subtasks according to the degree of sensitivity of the sensitive information in the subtasks from large to small, and sequentially divides the subtasks into different subtasks according to the number of preset levels. in the security level; S3. The blockchain server uses smart contract technology to limit the selection of the sensing task by the task receiving end; Said step S3 also includes: The smart contract is an electronic contract in the super ledger, and the rules, supervision and decision-making of the electronic contract are preset by the blockchain server; The step S3 is specifically: When the task receiver requests access to the perception task in the blockchain server, the electronic contract detects whether the security level of the task receiver is the same as the selected security level of the perception task, and if they are the same Then the task receiving end can receive the selected sensing task, otherwise, the task receiving end cannot receive the selected sensing task. 2. a kind of privacy protection method based on mobile crowd sensing technology according to claim 1, is characterized in that, described step S11 also comprises: The calculation of the betweenness centrality of the nodes is as follows: 3. a kind of privacy protection method based on mobile crowd sensing technology according to claim 1, is characterized in that, described step S11 also comprises: The calculation of the node local clustering coefficient is as follows: Among them, μG(v _i ) and ωG(v _i ) respectively represent the number of triangles and triangles in G(v _i ). 4. A kind of privacy protection method based on mobile crowd sensing technology according to claim 3, is characterized in that, described step S11 also comprises: The degree-based graph entropy of the nodes is calculated as follows: If α=1, then: Among them, n is the number of nodes in the graph, and m is the number of edges in the graph.