CN113328989B - End-cloud-cooperated vehicle insurance premium calculation model and method with user privacy protection - Google Patents

End-cloud-cooperated vehicle insurance premium calculation model and method with user privacy protection Download PDF

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CN113328989B
CN113328989B CN202110431694.4A CN202110431694A CN113328989B CN 113328989 B CN113328989 B CN 113328989B CN 202110431694 A CN202110431694 A CN 202110431694A CN 113328989 B CN113328989 B CN 113328989B
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郑欢欢
高丰
孙爽
王晓江
郁善金
杨涛
程宏才
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    • H—ELECTRICITY
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    • G06Q40/08—Insurance
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    • H—ELECTRICITY
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Abstract

The invention discloses a terminal cloud collaborative vehicle insurance premium calculation model and method for protecting user privacy, wherein the method comprises the following steps: s1, the end-side user constructs the transmitted data packet; s2, transmitting the data packet from the end side to the cloud side; s3, screening a cloud side shared data pool through encrypted data to obtain a candidate data set; s4, solving the recovery attributes of the candidate data set by using an equation set to obtain a matched data set; s5, generating a session key; s6, encrypting the vehicle insurance premium price of each matching item of the matching data set by using the session key, and transmitting the encrypted vehicle insurance premium price to the end-side user, and decrypting the vehicle insurance premium price by the end-side user; the end-side model includes: the system comprises a secret key generation module, a hash mapping module, a bloom matrix generation module, a heuristic matrix generation module and a decryption module; the cloud-side mold includes: the system comprises a cloud side shared data pool, a bloom filter, an encryption attribute recovery module, a session key generation module and a vehicle insurance premium price transmission module.

Description

End-cloud-cooperated vehicle insurance premium calculation model and method with user privacy protection
Technical Field
The invention relates to the technical field of cryptography and network security, in particular to a model and a method for calculating insurance premium of a user privacy protection vehicle under the cooperation of end cloud.
Background
The edge calculation is a new concept proposed after the cloud computing concept is developed, and means that the information of a user is acquired by using equipment with low cost and low power consumption at the edge, and simple data processing is performed on the edge. And transmitting the preprocessed data at the end side to the cloud end by using strong calculation force support and mass storage equipment of the cloud computing center, and performing subsequent complex processing. The edge computing sinks a part of functions in the cloud computing to edge nodes, performs primary analysis and processing on local data, undertakes the work of partial cloud, and reduces the pressure of a cloud center. The edge calculation can also reduce the time delay of various routing forwarding and network equipment processing in a complex network, obtain lower time delay and greatly reduce the bandwidth cost brought by network transmission and multistage forwarding.
Current user privacy protection mechanisms mainly use encrypted transmissions, requiring a trusted central management key exchange. The scheme aims at the problem of privacy protection attribute matching of an open system without a trusted center. And recommending vehicle insurance premium under the condition that the cloud end does not need decryption on the encrypted user attribute data at the edge side, so that the risk of data leakage of personal privacy data in network transmission is avoided.
Disclosure of Invention
In order to solve the defects of the prior art and achieve the purposes of preventing data leakage and accurately calculating the vehicle insurance premium in the process of transmitting end-side data to a cloud center under the condition of no credible center, the invention adopts the following technical scheme:
the method for calculating the vehicle insurance premium through the end cloud cooperation and user privacy protection comprises the following steps:
s1, the end-side user constructs a transmitted data packet, including: bloom matrix D, heuristic matrix M, hash function and encryption attribute set AsetSaid set of cryptographic attributes AsetFor generating a key hkey；
The encrypted end-side structured user attribute data comprises: generating an encryption key by using the attribute data specified by the user and using MD5 hash, wherein the encryption key is set to 512 bits;
s2, transmitting the user information data packet from the end side to the cloud side;
s3, screening a cloud side shared data pool through a data packet, wherein a user attribute set is contained in an attribute set of the cloud side shared data pool, namely the attribute type of the cloud side shared data pool contains the attribute type of a user, but the attribute values are possibly different, the default attribute data is None, a bloom filter is used for rapidly screening the cloud side shared data pool, and a bloom matrix D and a hash function are used for rapidly filtering to obtain a candidate data set;
s4, solving the candidate data set by using an equation set through a heuristic matrix M to recover default attribute data to obtain a matched data set;
s5, generating session key, encrypting attribute set A for each matching item of matching data setsetGenerating a session key from the attribute data in
Figure BDA0003031614620000021
S6, using the session key for the vehicle insurance premium price of each matching item of the matching data set
Figure BDA0003031614620000022
Encrypted and transmitted to the end-user, who uses the secret key hkeyThe vehicle insurance premium price is decrypted.
The adopted Encryption method is an AES (advanced Encryption Standard) symmetric Encryption method.
Further, the step S1 includes the following steps:
s11, user attribute data
Figure BDA0003031614620000023
From mnThe attribute type comprises Alpha, Beta and Gamma, wherein the Alpha attribute represents the attribute rapidly screened by using a bloom filter, and the Beta attribute represents the attribute which can be recovered by using an equation set; gamma represents other attribute types which do not need to be processed;
s12, the user generates a secret key and encrypts the attribute set AsetGenerating a key h from the attribute data in (1)keySaid A issetIs a user-specified set of attributes;
s13, use3 different hash functions H1,H2,H3Each a isiMapping to hash value:
H1(A)＝{h1(a1)mod p,h1(a2)mod p,…,h1(aalpha)mod p}
H2(A)＝{h2(a1)mod p,h2(a2)mod p,…,h2(aalpha)mod p}
H3(A)＝{h3(a1)mod p,h3(a2)mod p,…,h3(aalpha)mod p}
form a bloom matrix D ═ H1(A),H2(A),H3(A)]T；
S14, encrypting the Beta attribute in A, hashing the Beta attribute in A by using a password hashing function H, wherein the attribute vectors from the 1 st dimension to the Alpha dimension are Alpha attributes, the attribute vectors from the Alpha +1 st dimension to the Beta dimension are Beta attributes, and hashing the value of the attribute vector of the Beta attribute to obtain a fuzzy attribute vector: h (a) { halpha+1,halpha+2,…,hbeta}; generating a blur matrix F at the end sideγ×(γ+β)＝[Iγ×γ，Rγ×β]Wherein, the matrix I is a unit matrix of γ dimension, R is a random matrix of γ × β dimension, each element of which is a non-zero random integer, γ is a threshold value of the fuzzy attribute, which indicates that there are at most γ default values for the matched users, γ + β is equal to the dimension of the attribute Bta, and the user multiplies the fuzzy matrix F by the fuzzy attribute vector h (a) to obtain the matrix B: b ═ Fx [ h ]alpha+1,halpha+2,…,hbeta]TThe heuristic matrix M is formed by combining the matrixes F and B: m ═ F, B]。
Further, in step S2, the user combines the heuristic matrix M, the bloom matrix D, the hash function, and the encryption attribute set asetAnd packaging the data packets into data packet packets, and transmitting the packets to the cloud side by using a hypertext transfer security protocol.
Further, in step S3, the bloom filter is used to fast process the received packet by using the bloom filterScreening a cloud side shared data pool, and using a bloom matrix D and a hash function H1,H2,H3Performing rapid filtration, comprising the following steps:
s31, sharing each item D in the data pool on the cloud sidekMapping to hash value using hash function:
Figure BDA0003031614620000031
Figure BDA0003031614620000032
Figure BDA0003031614620000033
item DkRepresenting a piece of data with a complete set of attributes, e.g., a row in a database represents an entry and a column represents an attribute;
s32, removing H1(Dk)≠H1(A)，H2(Dk)≠H2(A)，H3(Dk)≠H3(A) Of the remaining entries as candidate data set, i.e. candidate entry data set D1。
Further, the step S4 includes the following steps:
s41, hashing the Beta attribute in the candidate data set by using a password hashing function H through the received data packet to obtain:
Figure BDA0003031614620000034
s42, for HkPossibly containing no more than γ unknown attributes, these unknowns being obtained by solving the following system of linear equations:
Figure BDA0003031614620000035
namely:
Figure BDA0003031614620000036
obtaining a candidate data set D of matching entries each having at most gamma default values2。
Further, in the step S6, the session key is used
Figure BDA0003031614620000037
Encrypting the matched item data set D using AES symmetric encryption method2In the k-th entry, attribute data of the vehicle insurance premium
Figure BDA0003031614620000038
Where v represents an attribute of the vehicle premium price and then data S is transmitted by the hypertext transfer security protocol httpsvTo end-side users, in which case the data sent to the users is used
Figure BDA0003031614620000039
Encrypted data, end-user usage hkeyDecryption SvSince AES is used for symmetric encryption, only the encrypted key is used
Figure BDA00030316146200000310
With a key h used by the userkeyThe plaintext can be decrypted by the same method, i.e. only if A is satisfiedsetThe data items with the same attribute data are used as the items for recommending the premium price of the user.
Further, using the decrypted plaintext information vkBy averaging:
Figure BDA00030316146200000311
where n represents the number of matching users, will
Figure BDA00030316146200000312
Returned to the user as a recommended price.
Further, the step S1 is to collect a set a according to the encryption attributesetHash the attribute vector value of (1), and use the hash value as the key h of AESkey(ii) a In step S5, according to the encryption attribute set a, through the received packetsetHashing the values of the attribute set of the matched items in the matching data set and using the hashed values as a key of the AES encryption method
Figure BDA0003031614620000041
Only the owned and user attribute set AsetThe items with the same privacy attribute and the same fuzzy attribute of the data can generate the secret key h consistent with the user attribute datakeyThe hash is based on the encryption attribute set AsetA hash value generated from the vector value of the attribute type of (2).
The vehicle insurance premium calculation model for the end-to-end user privacy protection with end cloud cooperation comprises a secret key generation module, a hash mapping module, a bloom matrix generation module, a heuristic matrix generation module and a decryption module;
the key generation module is used for generating a key by encrypting the attribute set;
the hash mapping module is used for mapping the user attribute data into a hash value;
the bloom matrix generating module is used for forming a bloom matrix from the hash values;
the heuristic matrix generation module is used for generating a heuristic matrix;
and the decryption module is used for decrypting the vehicle insurance premium price issued by the cloud side through the secret key.
The vehicle insurance premium calculation model comprises a cloud side shared data pool, a bloom filter, an encryption attribute recovery module, a session key generation module and a vehicle insurance premium price acquisition module;
the cloud side shares the data pool, and the attribute type of the data pool comprises the attribute type of the user;
the bloom filter is used for filtering by using a bloom matrix D and a hash function to obtain a candidate data set;
the encryption attribute recovery module is used for solving recovery attributes of the candidate data set by using an equation set to obtain a matched data set;
the session key generation module encrypts the attribute set A by each matching item of the matching data setsetGenerating a session key from the attribute data in
Figure BDA0003031614620000042
The vehicle insurance premium price transmission module uses the session key for the vehicle insurance premium price of each matching item of the matching data set
Figure BDA0003031614620000043
Encrypted and transmitted to the end-side user.
The invention has the advantages and beneficial effects that:
according to the invention, the data of the user is acquired by using equipment with low power consumption and low cost at the end side, the data of the user is not leaked, the attribute of the user is preprocessed, only some characteristics are uploaded to the cloud center, the attribute data are not uploaded, the secret key is dynamically generated according to the attribute of the user, an authoritative third party is not required to guarantee, the cloud center is an open architecture, and the accurate vehicle insurance premium is calculated by using the characteristic information of the user.
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Fig. 1 is a schematic structural view of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
The scheme aims at the problem of privacy protection attribute matching of an open system without a trusted center. The method for accurately calculating the vehicle insurance premium only uploads the characteristics of the user attribute data without uploading the user attribute data, and avoids the risk of data leakage of personal privacy data in network transmission. The attributes are divided according to different levels, a controllable matching mechanism is constructed, sensitive information is encrypted by using a high-level encryption standard, data characteristics of users are used in a cloud center to accurately match similar users, and the average value of insurance premium prices of the similar users is used as insurance premium recommended to the users.
As shown in fig. 1, the invention provides an end-cloud-collaborative accurate calculation model and method for protecting vehicle insurance premium. The model contains two roles: the cloud side shares a data pool and the end side user attribute data. The method comprises the steps that an end side uses structured user data to generate a key of a high-level encryption standard, a user information data packet is transmitted to a cloud side from the end side, a bloom filter is used for rapidly screening a cloud side shared data pool, an equation set is used for solving and recovering default attributes of a screened candidate data set, the cloud side generates a session key to encrypt insurance premium data values, finally, a user obtains encrypted insurance premiums through a hypertext transfer protocol, the user calculates vehicle insurance premium prices by using an averaging method to serve as vehicle insurance premium price recommendation of the user, and the vehicle insurance premium price recommendation method of the user is completed.
The feature of only transmitting the user attribute data from the end side to the cloud side in the present invention includes: the user collects attributes of the heuristic matrix M, Alpha, the bloom matrix D and the hash function H1,H2,H3Beta attribute set, random matrix for constructing equation set coefficient, matrix for using equation value obtained by multiplying equation matched attribute data and random matrix, encryption attribute set AsetAnd packaging the data into a user information data packet, and transmitting the data to the cloud side by using a hypertext transfer security protocol.
The user privacy protection provided by the invention realizes the following functions in the cloud computing:
(1) symmetric keys are generated based on the attribute data, and each requesting user dynamically generates the keys.
(2) A bloom filter is used for fast attribute matching of large data.
(3) And the random matrix encryption attribute is used for protecting the privacy of the user.
(4) And the random matrix encryption attribute is used for realizing the function of partial matching.
The accurate calculation model and the method for the vehicle insurance premium under the protection of the user privacy, which are provided by the invention, are different from the prior art:
(1) the scheme aims at the problem of privacy protection attribute matching of an open system without a trusted center.
(2) Private data is encrypted with a key generated using public attribute data that matches the natural share between users.
The invention provides an end-cloud-cooperated accurate calculation model and method for protecting vehicle insurance premium, which can be used for preliminarily analyzing user data by an end side, wherein symmetric encryption keys based on attribute data are respectively generated by an end-side user and cloud-side data, a third party is not required to supervise and manage, a cloud center can be used for matching a user group according to a threshold value under the condition that the data has default attributes, a terminal user obtains the encrypted data, the private key of the terminal user is used for decrypting the data, and the function of accurately calculating the vehicle insurance premium is carried out.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (6)

1. The method for calculating the vehicle insurance premium through the end cloud cooperation and user privacy protection is characterized by comprising the following steps:
s1, the end-side user constructs a transmitted data packet, including: bloom matrix D, openerSending matrix M, hash function and encryption attribute set AsetSaid set of cryptographic attributes AsetFor generating a key hkeyThe method comprises the following steps:
s11, user attribute data
Figure FDA0003499887030000011
From mnThe attribute type comprises Alpha and Beta, wherein the Alpha attribute represents the attribute screened by using a bloom filter, and the Beta attribute represents the attribute recovered by using an equation system;
s12, the user generates a secret key and encrypts the attribute set AsetGenerating a key h from the attribute data in (1)keySaid A issetIs a user-specified set of attributes;
s13, using 3 different hash functions H1,H2,H3Each a isiMapping to hash value:
H1(A)＝{h1(a1)mod p,h1(a2)mod p,…,h1(aalpha)mod p}
H2(A)＝{h2(a1)mod p,h2(a2)mod p,…,h2(aalpha)mod p}
H3(A)＝{h3(a1)mod p,h3(a2)mod p,…,h3(aalpha)mod p}
form a bloom matrix D ═ H1(A),H2(A),H3(A)]T；
S14, encrypting the Beta attribute in A, hashing the Beta attribute in A by using a password hashing function H, wherein the attribute vectors from the 1 st dimension to the Alpha dimension are Alpha attributes, the attribute vectors from the Alpha +1 st dimension to the Beta dimension are Beta attributes, and hashing the value of the attribute vector of the Beta attribute to obtain a fuzzy attribute vector: h (a) { halpha+1,halpha+2,…,hbeta}; generating a fuzzy matrix Fγ×(γ+β)＝[Iγ×γ，Rγ×β]Wherein the matrix I is a gamma-dimensional matrix of cells and R is a matrix of cells of size gamma x betaEach element of the machine matrix is a non-zero random integer, gamma is a threshold value of the fuzzy attribute and represents that at most gamma default values exist for matched users, gamma + Beta is equal to the dimension of Beta attribute, and the fuzzy matrix F and the fuzzy attribute vector H (A) are multiplied to obtain a matrix B: b ═ Fx [ h ]alpha+1,halpha+2,…,hbeta]TThe heuristic matrix M is formed by combining the matrixes F and B: m ═ F, B]；
S2, transmitting the data packet from the end side to the cloud side, and collecting the heuristic matrix M, the bloom matrix D, the hash function and the encryption attribute set AsetTransmitting to the cloud side;
s3, screening a cloud side shared data pool through a data packet, wherein the attribute type of the cloud side shared data pool comprises the attribute type of a user, screening the cloud side shared data pool by using a bloom filter, and filtering by using a bloom matrix D and a hash function to obtain a candidate data set, and the method comprises the following steps:
s31, sharing each item D in the data pool on the cloud sidekMapping to hash value using hash function:
Figure FDA0003499887030000012
Figure FDA0003499887030000013
Figure FDA0003499887030000014
item DkRepresenting a piece of data having a complete set of attributes;
s32, removing H1(Dk)≠H1(A)，H2(Dk)≠H2(A)，H3(Dk)≠H3(A) The remaining entries, i.e. alternative entry data sets D1Set of alternative item data D1As a candidate data set;
s4, solving the candidate data set by using an equation system through a heuristic matrix M to recover default attribute data to obtain a matched data set, and the method comprises the following steps:
s41, hashing the Beta attribute in the candidate data set by using a password hashing function H to obtain:
Figure FDA0003499887030000021
s42, for HkContains no more than γ unknown attributes, and these unknowns are obtained by solving the following system of linear equations:
Figure FDA0003499887030000022
namely:
Figure FDA0003499887030000023
obtaining a data set D of matched items on the candidate data set, each item having at most gamma default values2；
S5, generating a session key, and encrypting each matching encryption attribute set A of the matching data setsetGenerating a session key from the attribute data in
Figure FDA0003499887030000024
S6, using the session key for the vehicle insurance premium price of each matching item of the matching data set
Figure FDA0003499887030000025
Encrypted and transmitted to the end-user, who uses the secret key hkeyThe vehicle insurance premium price is decrypted.
2. The peer cloud assistant of claim 1The method for calculating the insurance premium of the vehicle with the privacy protection function of the user is characterized in that in the step S6, the key is converted into the conversation key
Figure FDA0003499887030000026
Encrypting the matching item data set D using an encryption method2In the k-th entry, attribute data S of the vehicle insurance premiumvThen the data SvTransmitted to the end-user, the end-user using hkeyDecryption Sv。
3. The end-cloud-collaborative vehicle insurance premium calculation method using user privacy protection according to claim 2, wherein the decrypted plaintext information v is usedkBy averaging:
Figure FDA0003499887030000027
where n represents the number of matching users, will
Figure FDA0003499887030000028
Returned to the user as a recommended price.
4. The method of claim 1, wherein the step of calculating the insurance premium of the vehicle based on the privacy attribute set A in step S1 is performed according to the encryption attribute set AsetHash the attribute vector value of (a), and use the hash value as a key hkey(ii) a In step S5, a set A is collected according to the encryption attributesetHashing the values of the set of attributes matching the items in the data set and using the hashed values as an encrypted key
Figure FDA0003499887030000029
Only having the same entry as the user attribute set data can generate a key h consistent with the userkeyThe hash value belongs to an encryption attribute set AsetA hash value generated from the vector value of the attribute type of (2).
5. The end-cloud-collaborative vehicle insurance premium calculation system for end-cloud-collaborative vehicle insurance premium calculation according to claim 1 includes a key generation module, a hash mapping module, a bloom matrix generation module, a heuristic matrix generation module, and a decryption module, and is characterized in that:
the key generation module is used for generating a key by encrypting the attribute set;
the hash mapping module is used for mapping the user attribute data into a hash value;
the bloom matrix generating module is used for forming a bloom matrix from the hash values;
the heuristic matrix generation module is used for generating a heuristic matrix;
and the decryption module is used for decrypting the vehicle insurance premium price issued by the cloud side through the secret key.
6. The end-cloud-collaborative vehicle insurance premium computing system for protecting the privacy of the cloud-side user is used for realizing the end-cloud-collaborative vehicle insurance premium computing method according to claim 1, and comprises a cloud-side shared data pool, a bloom filter, an encryption attribute recovery module, a session key generation module and a vehicle insurance premium price acquisition module, and is characterized in that:
the cloud side shares the data pool, and the attribute type of the data pool comprises the attribute type of the user;
the bloom filter is used for filtering by using a bloom matrix D and a hash function to obtain a candidate data set;
the encryption attribute recovery module is used for solving recovery attributes of the candidate data set by using an equation set to obtain a matched data set;
the session key generation module encrypts the attribute set A by each matching item of the matching data setsetGenerating a session key based on the attribute data
Figure FDA0003499887030000031
The vehicle insurance premium price transmission module uses the session key for the vehicle insurance premium price of each matching item of the matching data set
Figure FDA0003499887030000032
Encrypted and transmitted to the end-user.
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