CN109347829B - Group intelligence perception network truth value discovery method based on privacy protection - Google Patents

Group intelligence perception network truth value discovery method based on privacy protection Download PDF

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CN109347829B
CN109347829B CN201811233139.5A CN201811233139A CN109347829B CN 109347829 B CN109347829 B CN 109347829B CN 201811233139 A CN201811233139 A CN 201811233139A CN 109347829 B CN109347829 B CN 109347829B
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truth
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CN109347829A (en
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祝烈煌
张川
徐畅
张璨
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Beijing Institute of Technology BIT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
    • H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
    • H04L63/0478—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload applying multiple layers of encryption, e.g. nested tunnels or encrypting the content with a first key and then with at least a second key
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
    • H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
    • H04L63/045—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload wherein the sending and receiving network entities apply hybrid encryption, i.e. combination of symmetric and asymmetric encryption
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
    • H04L9/3236—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using cryptographic hash functions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
    • H04L9/0618—Block ciphers, i.e. encrypting groups of characters of a plain text message using fixed encryption transformation
    • H04L9/0631—Substitution permutation network [SPN], i.e. cipher composed of a number of stages or rounds each involving linear and nonlinear transformations, e.g. AES algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
    • H04L9/0643—Hash functions, e.g. MD5, SHA, HMAC or f9 MAC

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Abstract

The invention provides a privacy protection-based crowd sensing network truth value discovery method, and belongs to the technical field of internet of things crowd sensing. The privacy of the message is ensured by using an improved Paillier encryption algorithm in message transmission, the identity authentication of a user is ensured by using a one-way hash chain, and the high efficiency of output is ensured by using a super linear sequence. The identity authentication process can meet the authentication requirement of the terminal equipment in the truth finding environment and resist the attack of external attackers; the safety truth value discovery process can obtain real sensing data and meet the safety and privacy protection of the whole system. Compared with the traditional method, the method can protect the data and weight privacy of the user, greatly reduce the calculation and bandwidth consumption of the terminal equipment and improve the efficiency and safety of the whole system.

Description

Group intelligence perception network truth value discovery method based on privacy protection
Technical Field
The invention relates to a privacy protection-based crowd sensing network truth value discovery method, and belongs to the technical field of internet of things crowd sensing.
Background
The crowd sensing network integrates a communication technology and a sensor technology, and senses, collects, transmits and processes various environmental data and social information in the coverage field through various intelligent terminal terminals (such as a smart phone, a tablet computer, a smart watch and the like) carried by a large number of mobile terminals. With the continuous development and popularization of the intelligent terminals, the crowd sensing network can be widely applied to various fields such as environment sensing, public facility sensing, social sensing and the like, and is highly concerned by the industrial and academic circles.
However, the perceptual data collected by the mobile terminal is often not realistic due to environmental noise, hardware quality, even malicious fraud, and the like. For example, when collecting road congestion information, some terminals may provide wrong road condition information in order to obtain better travel experience. These false messages can mislead other pedestrians and vehicles and even bring life risks to them. Therefore, when performing crowd sensing, it is very necessary to discriminate the collected data to obtain real sensing data. To solve this problem, in recent years, truth discovery has been widely studied. Although the realisation algorithms found by truth are various, they all follow a most fundamental principle, i.e. if a terminal provides data that is closer to the real data, the terminal is given a higher weight; if a terminal has a higher weight, it contributes higher to the calculation of the true value when calculating the true value.
True value discovery techniques can accurately compute true perception data, however, existing true value discovery work rarely considers privacy protection of terminals. In fact, if the privacy of people is not protected, the terminal is not actively involved in various perception tasks. For example, collecting feedback information on medications may help hospitals to better provide medical services, but may compromise the health of the terminal. As another example, collecting answers to the public may be more effective in solving some troublesome problems, but may reveal the educational level of the participating terminals. Therefore, it is necessary to design an efficient and safe truth finding method.
The method is widely researched at home and abroad aiming at a safe and efficient truth finding method. For example, Miao et al proposed a cloud-based truth-finding privacy protection scheme in 2015, which utilizes a threshold Paillier encryption algorithm to encrypt the perceptual data of the terminals, and completes the decryption operation by distributing keys to t terminals. Although the scheme can well protect the privacy of the terminal, huge calculation overhead is brought to the mobile terminal. Zhou et al, shanghai university of transportation, proposed a data aggregation scheme based on wearable wireless communication terminals in 2015. In this scheme, each terminal is assigned a fixed key and a random number to perturb the original data. Based on the scheme, Xu et al of the electronics science and technology university propose an efficient and safe truth finding scheme in 2017, each terminal distributes random numbers to encrypt original data, and the sum of the random numbers is sent to a cloud server to encrypt the original data. However, if some terminals cannot submit data in a timely manner, their solutions do not work properly. In order to improve efficiency, Miao et al proposed a lightweight truth finding algorithm based on an unfeasible cloud platform in 2017. Similar to the Xu et al scheme, the perceptual data is also encrypted using random numbers. Specifically, the perturbation data is uploaded to cloud a, the random number is uploaded to cloud b, and the truth value is calculated through the cooperation of a and b. However, the above scheme has a problem in that the cloud b can restore the sensing data of the terminal by using a random number. Therefore, a safe, efficient, fault-tolerant truth discovery method is still lacking.
Disclosure of Invention
The invention aims to provide a real-value discovery method of a crowd sensing network based on privacy protection aiming at the authenticity of sensing data in the crowd sensing network and the privacy protection of a terminal. The basic principle is that an improved Paillier encryption algorithm is used in message transmission to ensure the privacy of messages, a one-way hash chain is used to ensure the identity authentication of a terminal, and a super-linear sequence is used to ensure the high efficiency of output.
The purpose of the invention is realized by the following technical scheme.
A group intelligence perception network truth value discovery method based on privacy protection comprises the following steps:
step one, the terminal registers with a trust center, the trust center selects a system security parameter ξ, and generates two large prime numbers p and q according to the security parameter, wherein p is 2p '+ 1, q is 2 q' +1, and p 'and q' areξ, the trust center calculates n-pq and λ -p 'q', the terminal registers in the trust center before participating in the task and acquires the related key hnWherein h is a random number; obtaining a random number zk(ii) a Obtaining a super-linear sequence
Figure BDA0001837584800000021
Secret key lambda and super-linear sequence to be decrypted by trust center
Figure BDA0001837584800000022
And sending the data to the cloud server.
Wherein the super-linear sequence
Figure BDA0001837584800000023
The generation rule of (1) is as follows: assume that the weight of terminal k is wkThe observed data of the observed entity m is
Figure BDA0001837584800000031
Suppose that the maximum value of the weighted data sum of at most K terminals in one crowd sensing task is not more than Q, namely
Figure BDA0001837584800000032
Then
Figure BDA0001837584800000033
In
Figure BDA0001837584800000034
j∈[2,M]Where M represents a maximum of M observation entities.
Meanwhile, the trust center selects a system security parameter l and generates a secure hash function according to the security parameter l
Figure BDA0001837584800000035
The trust center distributes a hash chain H for each terminal kkAnd hash heads of all terminals are combined
Figure BDA0001837584800000036
Send to the fog server. Wherein k represents the kth terminal in the crowd sensing; hash chain HkRepresenting a hash array held by the kth terminal, wherein the hash value
Figure BDA0001837584800000037
w is the total number of iterations.
Step two, the fog server randomly generates truth values of all observation entities
Figure BDA0001837584800000038
And sends it to all terminals participating in the task. Each terminal calculates the Euclidean distance between the true and observed values, i.e.
Figure BDA0001837584800000039
Wherein
Figure BDA00018375848000000310
Representing the observation of entity m by terminal k,
Figure BDA00018375848000000311
Figure BDA00018375848000000312
subsequently, the terminal selects a random number rkEncrypt the distance
Figure BDA00018375848000000313
Figure BDA00018375848000000314
In order to prevent the cloud server from decrypting the ciphertext, each terminal performs AES symmetric encryption on the ciphertext, wherein the symmetric key of the jth iteration is
Figure BDA00018375848000000315
The ciphertext is AES (E(s)k)). After performing the double encryption operation, the terminal will encrypt the ciphertext AES (E(s)k) ) and authentication information HkjAnd uploading to a fog server.
Step three, after receiving the message, the fog server receives the message through the previous timeTo verify the identity of the terminal, i.e. observe HkjWhether or not equal to
Figure BDA00018375848000000316
If the two are equal, the authentication is passed, otherwise the authentication is not passed. After the terminal identity is verified, the fog server decrypts all received ciphertexts according to the AES symmetric key of each terminal and decrypts the ciphertexts to obtain
Figure BDA00018375848000000317
Performing multiplicative aggregation, i.e. computing
Figure BDA00018375848000000318
And after the aggregation is finished, the fog server sends the result to the cloud server. The cloud server decrypts the aggregated result using λ, i.e.
Figure BDA00018375848000000319
Obtain the sum of the distances of all terminals, i.e.
Figure BDA00018375848000000320
And sends the decryption result to each terminal. The terminal calculates respective weight according to the aggregation result
Figure BDA00018375848000000321
Step four, based on the weight, the terminal calculates the weighted value of all the observation entities and uses the set super-linear sequence
Figure BDA0001837584800000041
Aggregating the weighted values of the entities, i.e.
Figure BDA0001837584800000042
Figure BDA0001837584800000043
Then, the terminal selects a random number rk2To swkIs encrypted to obtain
Figure BDA0001837584800000044
Figure BDA0001837584800000045
Selecting a random number rk3For the weight wkIs encrypted to obtain
Figure BDA0001837584800000046
Figure BDA0001837584800000047
The terminal carries out AES encryption on the two ciphertexts to obtain AES (E(s)wk) AES (E (w))k) And upload the two ciphertexts to the fog server.
Step five, after the mist server receives the ciphertext, the AES symmetric key of each terminal is used for decrypting the ciphertext to obtain E(s)wk) And E (w)k). The fog server in turn multiplies and aggregates the weighted values and weights of the terminals, i.e.
Figure BDA0001837584800000048
And
Figure BDA0001837584800000049
and sends the results to the cloud server. After receiving the ciphertext, the cloud server restores the ciphertext by using the key lambda, namely
Figure BDA00018375848000000410
And
Figure BDA00018375848000000411
the result is that
Figure BDA00018375848000000412
And
Figure BDA00018375848000000413
then, the cloud server restores the weighted sum of all the observation entities by using the super-linear sequence to obtain the weighted sum of each observation entity, namely
Figure BDA00018375848000000414
Cloud server update truth value
Figure BDA00018375848000000415
And transmits it to the respective terminals.
Wherein the weighted value reduction operation of each observation entity is defined as follows:
the cloud server obtains through decryption
Figure BDA00018375848000000416
Definition of
Figure BDA00018375848000000417
Cloud server pair XmCarry out amThe modulo operation restores the weighted data sum of the observation entity m, namely:
Figure BDA00018375848000000418
Figure BDA00018375848000000419
and step six, the terminal repeats the step two to the step five according to the updated true value.
And step seven, when the difference of the truth values before and after iteration does not exceed a set threshold value, the process is terminated. Thus, safe and efficient truth discovery is completed. The set threshold is preferably 0.0001.
Specifically, when some terminals cannot upload data continuously due to network conditions or damage of the terminals, the terminals are encrypted by using the same public key and are not associated with each other, so that loss of encrypted data of some terminals does not affect the final result of the system.
Advantageous effects
Compared with the prior safety truth value discovery technology, the method has the following beneficial effects:
1. the method is based on the existing truth value discovery algorithm, and the improved Paillier encryption algorithm is used in the message transmission process, so that the message privacy and the message encryption efficiency are ensured;
2. the protocol uses the iteration times for synchronization, so that the data received each time is the data required by the current iteration times, and the replay attack can be resisted under the condition of longer message transmission delay;
3. the protocol uses a one-way hash chain to ensure that the fog server can verify the identity of the terminal and resist external attack;
4. the protocol uses the super-linear sequence to aggregate the weighted data, so that the calculation and communication overhead of the terminal is reduced;
through the performance test on the real mobile terminal, the scheme is compared with the traditional public key scheme, and can obtain that: when the number of observation entities is 5 to 25, the calculation time of the scheme is 0.020s to 0.037s, and the communication overhead is 0.041KB to 0.061 KB; the conventional scheme takes 0.151s to 0.219s for calculation and 2.712KB to 8.746KB for communication.
Through performance tests on the fog server and the cloud server, the scheme is compared with the traditional public key scheme, and can be obtained that the calculation overhead of the scheme at the server end is 0.428s to 0.775s when the number of observation entities is 10 to 100 and 0.096s to 0.428s when the number of observation entities is 100 and the number of terminals is 100 to 1000 on the assumption that 1000 terminals exist; correspondingly, the conventional scheme takes 280.03s to 1269.72s, 244.283s to 280.03s in terms of computation time.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a flow chart of the computational process of the present invention;
fig. 3 is a diagram comparing the calculation overhead of the present invention and the conventional scheme on a mobile terminal when the number of entities is 5 to 25;
FIG. 4 is a diagram comparing the calculation cost of the present solution and the conventional solution at the server side when the number of terminals is 100 to 1000;
fig. 5 is a diagram comparing the computation overhead of the present solution and the conventional solution at the server side when the number of entities is 100 to 500.
Detailed Description
The method of the present invention will be described in further detail with reference to the accompanying drawings and examples.
Examples
The embodiment details the specific implementation process of the method in the crowd sensing scene.
The experiments were performed on one android phone configured with 1.5GHz 2GB RAM and two notebooks configured with 2.5GHz Intel Corei 716 GB RAM.
Fig. 1 is a system flow chart of the method of the present invention, fig. 2 is a specific implementation process of a true value discovery process, and as can be seen from fig. 1 and fig. 2, a process in which a terminal joins a true value discovery system is as follows:
step a, the terminal registers to a trust center to obtain a Hash chain HkAnd the associated secret key (Paillier's public key h)nAnd a random number zk). Meanwhile, the trust center sends the hash heads of all the terminals to the fog server
Figure BDA0001837584800000061
And random number
Figure BDA0001837584800000062
Sending the decrypted private key lambda to the cloud server;
step b, after the terminal receives the information, the symmetric key of the jth iteration process is generated according to the Hash chain
Figure BDA0001837584800000063
And according to the true value, the terminal calculates the distance between the observation value and the true value, and performs Paillier public key encryption and symmetric key encryption on the distance. After the calculation is completed, the terminal cipher text and the identity information of the terminal, namely AES (E(s)k) ) and HkjSending the data to a fog server;
step c, after the fog server receives the data, firstly, the identity of the terminal is verified, and the symmetric key of each terminal is calculated according to the Hash value and the random number
Figure BDA0001837584800000064
And use the secretDecrypting the ciphertext to obtain ciphertext E(s) encrypted by the Pailier public keyk). After decryption is completed, the fog server multiplies all ciphertext data and aggregates the ciphertext data to obtain the ciphertext data
Figure BDA0001837584800000065
And sending the data to a cloud server;
d, after the cloud server receives the data, the data is decrypted by using a private key lambda to obtain
Figure BDA0001837584800000066
And sending the result to all terminals;
step e, the terminal calculates respective weight information w according to the sum of the received distanceskAll weighted data are aggregated using a super-linear sequence. Subsequently, the terminal performs Paillier public key encryption and symmetric key encryption on the weight and the weighted data to obtain a ciphertext AES (E (w)k))，AES(E(swk) ) and identity information. After the operation is completed, the terminal sends the information to the fog server;
step f, after receiving the information, the fog server firstly checks the legality of the terminal, and executes key decryption and ciphertext aggregation operation to obtain
Figure BDA0001837584800000067
And
Figure BDA0001837584800000068
after the calculation is finished, the terminal sends the aggregated data to a cloud server;
and g, the cloud server decrypts the received accumulated ciphertext to obtain the weight sum and aggregated weighting information, and the cloud server uses the super-linear sequence to restore all terminal weighting data of each entity. Based on the weight sum and the weighted data sum of each entity, the cloud server calculates a true value of each entity and sends the true value to the terminal.
And h, repeating the steps b to g, and terminating the iteration when the truth value difference before and after the iteration does not exceed the set threshold value.
Fig. 3 is a line graph of efficiency of the android phone participating in the truth discovery process, with the abscissa being the number of observed entities and the ordinate being the total computational time. The black line is the time-consuming calculation under the present invention, and the red line is the time delay under the conventional scheme. As can be seen from fig. 3, compared with the conventional scheme, the secure and efficient truth-value discovery protocol provided by the invention has the advantages that the calculation time at the android mobile phone end is lower, and the efficiency is higher.
Fig. 4 is an efficiency line graph of the cloud and fog servers participating in the truth discovery process, where the number of observation entities is set to 100, the abscissa is the number of terminals, and the ordinate is the time consumed for calculation. As can be seen from fig. 4, compared with the conventional scheme, as the number of terminals increases, the computation time of both schemes increases, but a safe and efficient truth value of the present invention finds that the computation time of the protocol at the cloud and fog server ends is lower and more efficient, for example, when the number of terminals is 1000, the computation time of the scheme is 280.03s, while the computation time of the conventional scheme is only 0.428 s.
Fig. 5 is a line graph of efficiency of the cloud and fog servers participating in the truth discovery process, wherein the number of participating persons is set to be 1000, the number of entities on the abscissa and the time consumed by calculation on the ordinate are set. As can be seen from fig. 5, as the number of entities grows, the computation time of both schemes increases. Compared with the traditional scheme, the safe and efficient truth value discovery protocol has the advantages that the computing time consumption at the cloud and fog server ends is lower, and the efficiency is higher. Specifically, when the number of entities is 500, the calculation time of the present scheme is 0.775s, whereas the calculation time of the conventional scheme is 1269.72 s.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications may be made or equivalents may be substituted for some of the features thereof without departing from the scope of the present invention, and such modifications and substitutions should also be considered as the protection scope of the present invention.

Claims (2)

1. A group intelligence perception network truth value discovery method based on privacy protection is characterized by comprising the following steps:
step one, a terminal sends a messageThe trust center registers, selects system security parameters ξ, generates two large prime numbers p and q according to the security parameters, wherein the digits of p is 2p '+ 1, q is 2 q' +1, and p 'and q' are ξ, and then calculates n is pq, lambda is p 'q', the terminal registers in the trust center before participating in the task, and acquires a related key hnWherein h is a random number; obtaining a random number zk(ii) a Obtaining a super-linear sequence
Figure FDA0002455254750000011
Secret key lambda and super-linear sequence to be decrypted by trust center
Figure FDA0002455254750000012
Sending the data to a cloud server;
wherein the super-linear sequence
Figure FDA0002455254750000013
The generation rule of (1) is as follows: assume that the weight of terminal k is wkThe observed data of the observed entity m is
Figure FDA0002455254750000014
Suppose that the maximum value of the weighted data sum of at most K terminals in one crowd sensing task is not more than Q, namely
Figure FDA0002455254750000015
Then
Figure FDA0002455254750000016
In
Figure FDA0002455254750000017
Wherein M represents a maximum of M observation entities;
meanwhile, the trust center selects a system security parameter l and generates a secure hash function according to the security parameter l
Figure FDA0002455254750000018
The trust center distributes a hash chain H for each terminal kkAnd hash heads of all terminals are combined
Figure FDA0002455254750000019
Sending to a fog server; wherein k represents the kth terminal in the crowd sensing; hash chain HkRepresenting a hash array held by the kth terminal, wherein the hash value
Figure FDA00024552547500000110
Figure FDA00024552547500000111
w is the total number of iterations;
step two, the fog server randomly generates truth values of all observation entities
Figure FDA00024552547500000112
And sending the data to all terminals participating in the task; each terminal calculates the Euclidean distance between the true and observed values, i.e.
Figure FDA00024552547500000113
Figure FDA00024552547500000114
Wherein
Figure FDA00024552547500000115
Representing the observation of entity m by terminal k,
Figure FDA00024552547500000116
subsequently, the terminal selects a random number rkEncrypt the distance
Figure FDA00024552547500000117
In order to prevent the cloud server from decrypting the ciphertext, each terminal performs AES symmetric encryption on the ciphertext, wherein the symmetric key of the jth iteration is
Figure FDA00024552547500000118
The ciphertext is AES (E(s)k) ); after performing the double encryption operation, the terminal will encrypt the ciphertext AES (E(s)k) ) and authentication information HkjUploading to a fog server;
step three, after the fog server receives the message, the identity of the terminal is verified through the hash value received last time, namely H is observedkjWhether or not equal to
Figure FDA00024552547500000119
If the two are equal, the authentication is passed, otherwise, the identity authentication is not passed; after the terminal identity is verified, the fog server decrypts all received ciphertexts according to the AES symmetric key of each terminal and decrypts the ciphertexts to obtain
Figure FDA0002455254750000021
Performing multiplicative aggregation, i.e. computing
Figure FDA0002455254750000022
After the aggregation is completed, the fog server sends the result to a cloud server; the cloud server decrypts the aggregated result using λ, i.e.
Figure FDA0002455254750000023
Obtain the sum of the distances of all terminals, i.e.
Figure FDA0002455254750000024
And sending the decryption result to each terminal; the terminal calculates respective weight according to the aggregation result
Figure FDA0002455254750000025
Step four, based on the weight, the terminal calculates the weighted value of all the observation entities and uses the set super-linear sequence
Figure FDA0002455254750000026
Aggregating the weighted values of the entities, i.e.
Figure FDA0002455254750000027
Figure FDA0002455254750000028
Then, the terminal selects a random number rk2To swkIs encrypted to obtain
Figure FDA0002455254750000029
Selecting a random number rk3For the weight wkIs encrypted to obtain
Figure FDA00024552547500000210
The terminal carries out AES encryption on the two ciphertexts to obtain AES (E(s)wk) AES (E (w))k) And uploading the two ciphertexts to a fog server;
step five, after the mist server receives the ciphertext, the AES symmetric key of each terminal is used for decrypting the ciphertext to obtain E(s)wk) And E (w)k) (ii) a The fog server in turn multiplies and aggregates the weighted values and weights of the terminals, i.e.
Figure FDA00024552547500000211
And
Figure FDA00024552547500000212
and sending the result to a cloud server; after receiving the ciphertext, the cloud server restores it using the secret key λ, i.e.
Figure FDA00024552547500000213
And
Figure FDA00024552547500000214
the result is that
Figure FDA00024552547500000215
And
Figure FDA00024552547500000216
then, the cloud server restores the weighted sum of all the observation entities by using the super-linear sequence to obtain the weighted sum of each observation entity, namely
Figure FDA00024552547500000217
Cloud server update truth value of
Figure FDA00024552547500000218
And sends it to each terminal;
wherein, the weighted value reduction operation of each observation entity is defined as follows:
the cloud server obtains through decryption
Figure FDA00024552547500000219
Definition of
Figure FDA00024552547500000220
Cloud server pair XmCarry out amThe modulo operation restores the weighted data sum of the observation entity m, namely:
Figure FDA00024552547500000221
Figure FDA00024552547500000222
step six, the terminal repeats the step two to the step five according to the updated truth value;
and step seven, when the difference of the truth values before and after iteration does not exceed a set threshold value, the process is terminated.
2. The privacy protection-based crowd sensing network truth discovery method according to claim 1, wherein the set threshold of step seven is 0.0001.
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