A privacy protection protocol system and method for sharing economy in fog enhanced internet of things

Abstract

The application discloses a kind of fog enhanced internet of things in sharing economy privacy protection protocol system and method, including: user layer, equipment layer, fog layer and operator layer;User layer connects fog layer, the fog layer connects equipment layer, the operator layer is connected user layer and equipment layer respectively;A kind of fog enhanced internet of things in sharing economy privacy protection protocol method;Step S1: request aggregation distribution equipment request total number of people;Step S2: according to equipment request total number of people, cost is divided equally;Step S3: whether equipment request total number of people reaches the threshold value of equipment response, if yes, then enter step S4;If not, end;Step S4: data is handled;Solve the fairness problem of consumption and the security problem of privacy when user cooperates.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fog enhanced Internet of Things, and particularly to a privacy protection protocol system and method for sharing economy in fog enhanced Internet of Things. BACKGROUND

[0002] With the development of technology, more and more devices are connected to the Internet of Things, making the use of sharing collaboration in the Internet of Things more and more frequent, such as knowledge management, smart contract and other applications. Since these applications require multiple participants to jointly process, users need to jointly bear the cost of requesting devices in the entire process.

[0003] The problem of fairness of cost sharing by users can be solved by using a platform for centralized processing of data. However, since a large number of Internet of Things devices are connected, and the data needs to be processed centrally, this puts high requirements on the computing power and communication load of the Internet of Things. Fog computing is a desirable way to solve the problem of computing power and communication load of the Internet of Things. By adding fog nodes as data processing platforms in the Internet of Things, the processing capacity of the Internet of Things for data can be greatly improved.

[0004] For fog enhanced Internet of Things, since untrusted fog nodes are introduced, participants are concerned about their privacy security. Users do not want their request information for Internet of Things devices and the services obtained to be known by others. Therefore, how to enable participants to share collaboration while ensuring the fairness of cost allocation and privacy security in fog enhanced Internet of Things is a problem that the present application focuses on solving. SUMMARY

[0005] The present application provides a privacy protection protocol system and method for sharing economy in fog enhanced Internet of Things, solving the problems of fairness of consumption and privacy security of users when collaborating.

[0006] The technical solutions of the present application are as follows:

[0007] A privacy protection protocol system for sharing economy in fog enhanced Internet of Things, comprising

[0008] a user layer, a device layer, a fog layer and an operator layer;

[0009] The user layer is connected to the fog layer, the fog layer is connected to the device layer, and the operator layer is connected to the user layer and the device layer, respectively.

[0010] Preferably, a privacy protection protocol method for sharing economy in fog enhanced Internet of Things, comprising the following steps:

[0011] Step S1: aggregating and distributing the total number of device requests;

[0012] Step S2: fairly dividing the cost according to the total number of device requests;

[0013] Step S3: judging whether the total number of device requests reaches the threshold value of device response, if yes, paying the cost for the device and obtaining the storage data in the device; if no, ending;

[0014] Step S4: processing the data.

[0015] Preferably, step S1 comprises the following sub-steps:

[0016] Sub-step S11: transmitting the user request vector and the random number generated by the user to FS2 by using the public key encryption;

[0017] Sub-step S12: aggregating the user requests in FS2 by using the additive homomorphism of Paillier encryption algorithm and sending them to FS1 for decryption;

[0018] Sub-step S13: after decryption in FS1, judging whether the number of requests exceeds the threshold value of the Internet of Things device, and sending the judgment result to FS2 by multiplying the request aggregation in sub-step S12;

[0019] Sub-step S14: in FS2, the total number of requests for each device is allocated to the user according to the user's request by using the multiplicative homomorphism of Paillier encryption algorithm, and then sent to FS1 for decryption after being polluted with a random number.

[0020] Preferably, step S2 comprises the following sub-steps:

[0021] Sub-step S21: the user receives the total number of requested devices polluted by a random number from FS1;

[0022] Sub-step S22: since the random number is generated by the user himself, the user can restore the total number of requested devices;

[0023] Sub-step S23: according to the total number of requested devices, the price disclosed by the device is evenly distributed according to the number of people, so as to realize the fair evaluation of the cost of device request under the condition that the users do not communicate with each other.

[0024] Preferably, step S3 comprises the following sub-steps:

[0025] Sub-step S31: transmitting the vector in FS1 to the device, and multiplying the data stored in each device with the vector;

[0026] Sub-step S32: encrypting and transmitting the multiplied vector to FS2.

[0027] Preferably, step S4 comprises the following sub-steps:

[0028] Sub-step S41: in FS2, the device response data is decrypted in FS1 using the random number to obtain a decrypted vector;

[0029] Sub-step S42: for the decrypted vector, the random number is multiplied to obtain a k-row N-column matrix and is transmitted to FS2;

[0030] Wherein k is the number of users, and N is the number of devices;

[0031] Sub-step S43: in FS2, the random number multiplied in the last stage in FS2 is eliminated;

[0032] Sub-step S44: using the homomorphic multiplication of Paillier, the data in the device is distributed to the user who requests it;

[0033] Sub-step S45: in order to protect privacy, the user generates his own random number matrix,

[0034] Sub-step S46: according to the homomorphic addition of Pailler, the matrix is polluted and sent to FS1 for decryption;

[0035] Sub-step S47: after receiving the decryption, the data matrix finds the position of its own data in the data, and places the data in the position;

[0036] Sub-step S48: after placing the data in the position, if the data needs to be recovered, since the random number is generated by the user himself, the data of the user is recovered.

[0037] The privacy protection protocol system and method of the shared economy in the fog enhanced Internet of Things have the following advantages:

[0038] The user can safely obtain the storage data of the requested device without revealing the request schedule, and can reach an agreement with other users on public cost payment. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 It is a system architecture diagram of the privacy protection protocol of the shared economy in the fog enhanced Internet of Things.

[0040] Figure 2 It is a device sharing diagram of the privacy protection protocol of the shared economy in the fog enhanced Internet of Things.

[0041] Figure 3 The method of the privacy protection protocol of the shared economy in the fog enhanced Internet of Things includes a request aggregation and distribution step and a cost fair sharing step.

[0042] Figure 4The method for implementing the privacy protection protocol of the sharing economy in the fog enhanced Internet of Things includes a data processing step, a request aggregation and distribution step.

[0043] Figure 5 The graph of the time required by the aggregation and distribution step, the device response step and the data processing step under different numbers of users in the embodiment of the application.

[0044] Figure 6 The graph of the time required by the aggregation and distribution step, the device response step and the data processing step under different numbers of devices in the embodiment of the application.

[0045] Figure 7 The graph of the time required by the aggregation and distribution step, the device response step and the data processing step under different device request proportions in the embodiment of the application. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions and advantages of the application clearer, the application is further described below according to the drawings and examples.

[0047] As shown in Figure 1 , the system architecture of the privacy protection protocol of the sharing economy based on the fog enhanced Internet of Things includes a user layer, a device layer, a fog layer and an operator layer. The user layer is composed of users accessing the Internet of Things, denoted as U={u1, u2,..., u k}, each user u i has a request vector V i ={v i1 ,...,v iN} for the Internet of Things devices, indicating whether to make a request for the device, where v in ∈{0,1}. The device layer is composed of devices in the Internet of Things, denoted as I={I1, I2,..., I N}. Each device has storage capacity, and the data stored by the nth device is denoted as D n , with an upper limit of φ, 0≤D n ≤φ. The request price of the device is public, denoted as The number of requests exceeds the threshold τ of the device, and the device needs to pay a fee. The fog layer is composed of two fog servers FS1 and FS2, where FS1 holds the private key and has decryption capability, and FS2 can only encrypt. The operator layer is deployed above the user layer, the device layer and the fog layer, and mainly performs the work of security coefficient setting, key distribution and receiving fee. The entire system conforms to the honest and curious model and will not actively launch an attack, but will only pry into the privacy of others as much as possible according to the protocol.

[0048] As shown in Figure 2As shown, this invention enables the sharing of IoT facilities with storage capabilities. Each user generates device requests at different times, and these requests are known only to the user. Through encrypted aggregation, the fog server obtains coarse-grained aggregated transmission. At this point, the fog server only knows which devices were requested and the total number of requests, but not which specific users sent the requests, ensuring that the user request schedule is not leaked. The total number of requests for each facility is then sent to the users who made the requests, allowing them to know how many users are sharing the facility with them, and thereby achieving cost sharing for the facility. This ensures fair cost sharing even without user interaction.

[0049] like Figure 3 As shown, in the request aggregation and allocation step and the cost fair sharing step proposed in this invention, the user's request vector is first encrypted. For user i, E(V) can be obtained. i )={E(v i1 ),E(v i2 ),…,E(v iN The user layer generates and encrypts a random number δ, then sends the encrypted request vector and the random number to FS2. FS2 leverages the homomorphism of the Paillier system to aggregate the requests.

[0050]

[0051] The encrypted aggregate is sent to FS1 for decryption to obtain:

[0052]

[0053] This allows us to obtain the total number of requests for each device without exposing the specific user's request schedule. Next, we determine if the number of requests exceeds the device's threshold to decide whether the user needs to pay. This is done using a vector C = {c1, c2, ..., c...}. N} Perform a judgment, where

[0054]

[0055] 1 indicates the number of people exceeds the threshold, and 0 indicates the number of people does not exceed the threshold. The judgment result is then assigned to the aggregation vector. And transmit it to FS2.

[0056] In FS2, Assign devices to users according to their requests, ensuring that only users who requested the device receive the total number of users who requested the device. Using the multiplicative homomorphism of the Paillier system, we can obtain:

[0057]

[0058] To protect the security of the user request information, the matrix needs to be contaminated with a random number δ before decryption:

[0059]

[0060] FS1 receives the contaminated matrix and decrypts it to get and sends it to the user, and the user i recovers and gets Take the inverse of it to get T i '(0 value remains unchanged). In the case where the user knows the device price P I , the price to be paid for user i is:

[0061]

[0062] So far, the request aggregation allocation step and the cost fair sharing step are completed.

[0063] As Figure 4 shown, in the data processing step and the request aggregation allocation step proposed by the application, first, the device response is judged by using the judgment vector C, and it is multiplied by the data stored in the Internet of Things device to get the data vector C·I={c1D1,c2D2,…,c N D N}, where there is:

[0064]

[0065] For the devices that do not respond, the data stored in the transmission process is set to 0, and the response of the device is reflected in turn. After the data vector is encrypted, Q={E(c1D1),E(c2D2),…,E(c N D N )} is obtained and transmitted to FS2. The vector Q is contaminated with the random number ε generated in FS2 to get:

[0066] Q con ={E(c1D1) ε ,E(c2D2) ε ,…,E(c N D N ) ε}

[0067] ={E(εc1D1),E(εc2D2),…,E(εc N D N )}

[0068] and then transmitted to FS1 for decryption to get D(Q con )={εc1D1,εc2D2,…,εc ND N} Since the random number ε is unknown to FS1, the data is guaranteed not to be leaked. In the user layer, the random number matrix σ 1×k = {σ1, σ2, …, σ k} and λ 1×k = {λ1, λ2, … λ k} are independently generated by each user, and the user only knows the random number generated by himself and sends it to FS1 and FS2 respectively. By using σ 1×k , the Q con is contaminated, and we can get:

[0069]

[0070] Then the matrix is transmitted to FS2, and since σ 1×k is unknown to FS2, the data is guaranteed to be safe.

[0071] In FS2, the matrix is first simplified to get:

[0072]

[0073] Then the data is allocated to the user who requests it, and for user i, we can get:

[0074]

[0075] Then by using λ i , it is contaminated to get:

[0076]

[0077] Therefore, the final encrypted data matrix can be obtained as:

[0078]

[0079] Transmit it to FS1 for decryption to get:

[0080]

[0081] Send the R matrix to the user layer, and the user knows the location of the data requested in the matrix. The user can use the random number σ n and λ n generated by himself to restore the data requested:

[0082] R i ' = (R i - λ i )σ i -1 = {v i1 c1D1, v i2 c2D2, …, viN c N D N}

[0083] Guaranteed in not leaking user request schedule conditions, safe and accurate transmission of user requested data.

[0084] So far, the device response step and data processing step are completed.

[0085] The symbol description involved in the above formula derivation is shown in Table 1.

[0086]

[0087]

[0088] Simulation results:

[0089] As Figure 5 shown, the influence of different number of users on the time required for the aggregation allocation step, the device response step and the data processing step is embodied. It is found that as the number of users increases, the time required for the aggregation allocation step and the data processing step also increases, showing a positive correlation. The time required for the device response step is not affected by the number of users and remains basically unchanged.

[0090] As Figure 6 shown, the influence of different number of Internet of Things devices on the time required for the aggregation allocation step, the device response step and the data processing step is embodied. It is found that as the number of Internet of Things devices increases, the time required for the aggregation allocation step, the device response step and the data processing step also increases, showing a positive correlation. The influence of the aggregation allocation step and the data processing step on the device response step is relatively greater, and the time required for the aggregation allocation step and the data processing step increases faster.

[0091] As Figure 7 shown, the influence of different device request ratio μ on the time required for the aggregation allocation step, the device response step and the data processing step is embodied. It is found that as the request ratio increases, the time required for the aggregation allocation step, the device response step and the data processing step does not change significantly and remains basically unchanged. This shows that the time required for the three steps has nothing to do with the request ratio of the Internet of Things devices.

[0092] Those skilled in the art will realize that the embodiments described herein are for the purpose of helping the reader understand the implementation method of the present application and should be understood as not limiting the protection scope of the present application to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations according to the technical inspiration disclosed in the present application without departing from the essence of the present application, and these modifications and combinations are still within the protection scope of the present application.

Claims

1. A privacy protection protocol method for sharing economy in fog enhanced Internet of Things, characterized in that, The method comprises the following steps: Step S1: Requesting the total number of people requested by the aggregation allocation device; Step S2: Fairly dividing the cost according to the total number of people requested by the device; Step S3: Determining whether the total number of people requested by the device reaches the threshold value of the device responding, if yes, entering step S4; if no, ending; Step S4: Processing the data; Step S1 comprises the following sub-steps: Sub-step S11: Using the public key to encrypt the user request vector and the random number generated by the user to transmit them to FS2; Sub-step S12: Using the additive homomorphism of the Paillier encryption algorithm to aggregate the user requests in FS2 and send them to FS1 for decryption; Sub-step S13: After decryption in FS1, determining whether the number of requesters exceeds the threshold value of the Internet of Things device, and sending the determination result to FS2 for multiplication with the request aggregation in sub-step S12; Sub-step S14: In FS2, using the multiplicative homomorphism of the Pailler encryption algorithm, distributing the total number of requests of each device to the users according to the user's request, and sending the data to FS1 for decryption after the data is polluted with a random number; The step S2 comprises the following sub-steps: Sub-step S21: The user receives the total number of request devices polluted by the random number from FS1; Sub-step S22: Since the random number is generated by the user himself, the user can restore the total number of request devices; Sub-step S23: According to the total number of request devices, the price disclosed by the device is evenly distributed according to the number of people, which realizes the fair evaluation of the cost of device request under the condition that the users do not communicate with each other; The step S3 comprises the following sub-steps: Sub-step S31: Transmitting the vector in FS1 to the device, and multiplying the data stored in each device with the vector; Sub-step S32: Encrypting and transmitting the multiplied vector to FS2; The step S4 comprises the following sub-steps: Sub-step S41: In FS2, the random number is used to transmit the device response data to FS1 for decryption to obtain the decrypted vector; Sub-step S42: For the decrypted vector, the random number is multiplied to obtain a k-row N-column matrix and transmit it to FS2; Wherein k is the number of users, and N is the number of devices; Sub-step S43: In FS2, the random number multiplied in the last stage in FS2 is eliminated; Sub-step S44: Using the homomorphic multiplication of Paillier, the data in the device is distributed to the user who requests it; Sub-step S45: In order to protect privacy, the user generates his own random number matrix, Sub-step S46: According to the homomorphic addition of Pailler, the matrix is polluted and sent to FS1 for decryption; Sub-step S47: After receiving the decryption, the data matrix finds the position of its own data in the data and places the data in the position; Sub-step S48: After placing the data in the position, if the data needs to be restored, since the random number is generated by the user himself, the data of the user is restored.

2. A privacy protection protocol system for sharing economy in fog enhanced Internet of Things, characterized in that, The system can be used to implement the privacy protection protocol method of the sharing economy in the fog enhanced Internet of Things in claim 1, specifically comprising: User layer, device layer, fog layer and operator layer; The user layer is connected with the fog layer, the fog layer is connected with the device layer, and the operator layer is connected with the user layer and the device layer respectively.

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