CN111526155A - System for protecting user privacy in social network and optimal path matching method - Google Patents

System for protecting user privacy in social network and optimal path matching method Download PDF

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CN111526155A
CN111526155A CN202010363822.1A CN202010363822A CN111526155A CN 111526155 A CN111526155 A CN 111526155A CN 202010363822 A CN202010363822 A CN 202010363822A CN 111526155 A CN111526155 A CN 111526155A
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张明武
陈誉
丁勇
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Guilin University of Electronic Technology
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Abstract

The invention relates to a system for protecting user privacy in a social network and an optimal path matching method. The method comprises the following steps: the trusted center generates system parameters and a secret key and sends the system parameters to the server and the user; the user registers; the server registers; a user constructs vertex information and weight information and sends ciphertext information generated in the process of constructing the vertex information and the weight information to a server; the server is used for constructing a social graph according to the vertex information and the weight information; a query user in the users uploads a starting terminal mark to a server; the server generates a path sequence and a weight sequence according to the social graph and the starting and ending point identification; the server sends the weight sequence to the inquiry user so that the inquiry user can determine the position of the ciphertext; and the inquiry user determines the optimal path from the path sequence by adopting an inadvertent transmission mode according to the ciphertext position. The invention can ensure that the privacy of the user is not revealed during path query, and has the advantages of high query speed, accurate and efficient processing.

Description

System for protecting user privacy in social network and optimal path matching method
Technical Field
The invention relates to the technical field of social networks, in particular to a system for protecting user privacy in a social network and an optimal path matching method.
Background
In order to realize various intelligent services, such as recommendation service, sharing service and query service, the online social network needs to perform information association query and matching by using personal attribute information of users to obtain optimal recommendations in the social network. However, these user attribute information implies the personal privacy of many users, such as the age, sex, work unit, place of residence, and other sensitive information of the users. Malicious attackers can obtain, analyze and use the data for illegal operations by means of snooping and the like. Therefore, how to protect the privacy of users in social networks is an important issue.
In the social network, all users upload their private information to a cloud server. The user provides the source point information and the end point information, so that the propagation path between the two coordinates can be inquired, wherein the optimal propagation path represents that the propagation cost of the user is smaller and the propagation is more effective. Through the optimal propagation path, the user can rapidly and efficiently propagate a certain message to the target user. In order to protect privacy, user information is usually uploaded after being encrypted, and propagation cost (defined as weight value) between users needs to be compared in an outsourcing environment. The whole process should ensure that the server cannot know the sensitive data of the user and the system user cannot obtain any information except the propagation path. Some methods for solving the above problems, such as a path query method based on a homomorphic encryption algorithm, also appear; the homomorphism property enables the ciphertext obtained by encrypting to be subjected to certain operation to be equal to the ciphertext obtained by performing another operation on the encrypted plaintext. However, in the existing path query scheme, firstly, the definition of weight information between vertexes is not accurate enough, the interaction between the vertexes is not considered, and the purpose of path query cannot be clearly expressed; secondly, the query speed is positively correlated with the number of vertexes, so that the practicability of the query of the large social network cannot be guaranteed or the balance between accuracy and efficiency needs to be made.
Disclosure of Invention
The invention aims to provide a system for protecting user privacy in a social network and an optimal path matching method, which can ensure that propagation paths of any two vertexes of a large social network can be quickly inquired, the inquiry speed does not depend on the number of users, and the privacy protection safety is improved.
In order to achieve the purpose, the invention provides the following scheme:
a system for protecting user privacy in a social network, comprising: the system comprises a trusted center, a server and a user, wherein the trusted center is used for generating system parameters and a secret key and sending the parameters to the server; the method comprises the steps that after the user registers, vertex information and weight information are constructed, ciphertext information generated in the process of constructing the vertex information and the weight information is sent to a server, the server is used for constructing a social graph according to the vertex information and the weight information after registering, a query user in the user is used for providing identification marks of the user and a target user, the server is used for querying all propagation paths and corresponding weights, and an optimal path is provided for the query user through an inadvertent transmission mode.
An optimal path matching method for protecting user privacy in a social network comprises the following steps:
the trusted center generates system parameters and a secret key and sends the system parameters to the server and the user;
the user registers;
the server registers;
the user constructs vertex information and weight information and sends ciphertext information generated in the process of constructing the vertex information and the weight information to the server;
the server is used for constructing a social graph according to the vertex information and the weight information;
a query user in the users uploads a starting end point identifier to the server;
the server generates a path sequence and a weight sequence according to the social graph and the starting and ending point identification;
the server sends the weight value sequence to the inquiry user so that the inquiry user can determine the ciphertext position;
and the inquiry user determines an optimal path from the path sequence by adopting an inadvertent transmission mode according to the ciphertext position.
Optionally, the trusted center generates system parameters and a secret key, and sends the system parameters to the server and the user, which specifically includes:
acquiring a safety parameter kappa;
according to the security parameters, calculating a master public key mpk and a master key msk of the encryption algorithm, wherein mpk is (g)msk,g,p)，
Figure BDA0002476024190000021
p is a large prime number and satisfies | p | ═ k,
Figure BDA0002476024190000022
is [1, p-1 ]]In an arbitrary integer, g is
Figure BDA0002476024190000031
A generator of (2); selecting a random number R, wherein | R | < | p |/3;
obtaining a key pair (sk)1,sk2) The key pair includes a first key sk1And a second key sk2Wherein
Figure BDA0002476024190000032
sk1+sk2＝msk mod(p-1)；
randomly acquiring n user key vectors
Figure BDA0002476024190000033
Wherein,
Figure BDA0002476024190000034
and issuing parameter information, wherein the parameter information comprises the master public key, the generator, the prime number and the random number.
Optionally, the registering of the user specifically includes:
sending a first registration request;
the trusted center randomly selects an integer from the integer sequence as the identity ID of the current user according to the first registration requestiAnd combining the key vector
Figure BDA0002476024190000038
Returning to the current user;
sending the ID to all users connected with the current useri；
The inquiring user obtains the IDiThen sending a second registration request;
the trusted center returns the first key sk to the inquiring user according to the second registration request1。
Optionally, the registering by the server specifically includes:
sending a third registration request;
the trusted center returns the second key sk to the server according to the third registration request2。
Optionally, the user constructs vertex information and weight information, and sends ciphertext information generated in the process of constructing the vertex information and the weight information to the server, and the method specifically includes:
obtaining the USERiAttribute information of (2);
binarizing the attribute information through one-hot coding to enable the value of only one bit in a binary value corresponding to each dimension attribute to be 1; the user converts all discrete attributes of an individual into an attribute vector of length w
Figure BDA0002476024190000035
The USER USERiAccording to the key vector
Figure BDA0002476024190000036
Encrypting the attribute vector
Figure BDA0002476024190000037
Deriving vertex information v in a social networki；
USER for each connected USERjThe current USER USERiSending an application to the trusted center;
the trusted center returns a weight key according to the application;
calculating a weight ciphertext by using the encryption homomorphism property and the weight key, and sending the weight ciphertext to a server;
and the server integrates the ciphertext to obtain weight information.
Optionally, the server generates a path sequence and a weight sequence according to the social graph and the start and end point identifier, and specifically includes:
determining a vertex v corresponding to a starting point identifier in the social graphsAnd is defined as a first layer set of starting vertices S1(ii) a Finding the vertex v corresponding to the terminal point identificationtAnd defined as a first layer termination vertex set T1；
Determining the first layer starting vertex set S1And the first layer termination vertex set T1Is denoted by vuWherein v isu∈{S1∩T1Querying a vertex v according to the social graphsAnd vuAnd vuAnd vtThe weights between them are respectively marked as Es,uAnd Eu,tA 1 is mixing Es,u·Eu,tAdded to the weight sequence
Figure BDA0002476024190000041
In, query vertex v simultaneouslyuCorresponding identification IDuWill IDuJoin to Path sequence
Figure BDA0002476024190000042
Performing the following steps;
determining the first set of starting vertices S from the social graph1Each vertex v ofiAll connected vertices v ofjAnd is defined as the second layer initial vertex set S2At the same time vi→vjAdding to a set of prepositioned vertices P1；
Determining the second layer starting vertex set S2And the first layer termination vertex set T1Is updated by vuWherein v isu∈{S2∩T1At the same time in the set of front vertices P1In finding vuAnd is denoted by viSequentially querying vertexes v according to the social graphsAnd vi、viAnd vuAnd vuAnd vtThe weights between are respectively marked as Es,i、Ei,uAnd Eu,tA 1 is mixing Es,i·Ei,u·Eu,tAdded to the weight sequence
Figure BDA0002476024190000043
In, query vertex v simultaneouslyiAnd vuCorresponding identification IDiAnd IDuWill IDi·R+IDuJoin to Path sequence
Figure BDA0002476024190000044
Performing the following steps;
determining the first-tier set of termination vertices T from the social graph1Each vertex v ofi′All connected vertices v ofj′And is defined as a second layer termination vertex set T2At the same time vi′→vj′Adding to a set of postpositional vertices P2；
Determining the second layer starting vertex set S2And the second layer termination vertex set T2Is updated by vuWherein v isu∈{S2∩T2At the same time in the set of front vertices P1In finding vuAnd is denoted by viAt said set of post-vertices P2In finding vuIs denoted by vi′Sequentially querying vertexes v according to the social graphsAnd vi、viAnd vu、vuAnd vi' and vi' and vuThe weights between are respectively marked as Es,i、Ei,u、Eu,i′And Ei′,tA 1 is mixing Es,i·Ei,u·Eu,i′·Ei′,tIs added to
Figure BDA0002476024190000045
In, query vertex v simultaneouslyi、vi' and vuCorresponding identification IDi、IDi' and IDuWill IDi·R2+IDu·R+IDi′Is added to
Figure BDA00024760241900000512
In (1), get the path sequence
Figure BDA0002476024190000051
Sum weight sequence
Figure BDA0002476024190000052
Optionally, the sending, by the server, the weight sequence to the querying user so that the querying user determines a ciphertext position includes:
according to the second key sk2Decrypting the weight sequence
Figure BDA0002476024190000053
Each element of (a) obtains a first sequence of decryption weights
Figure BDA0002476024190000054
And sending to the inquiring user;
for the inquiryThe user according to the first key sk1Decrypting the first weight sequence
Figure BDA0002476024190000055
Each element of (2) to obtain a second decryption weight sequence
Figure BDA0002476024190000056
According to the second decryption weight sequence
Figure BDA0002476024190000057
And obtaining the ciphertext position.
Optionally, the querying user determines an optimal path from the path sequence by adopting an inadvertent transmission manner according to the ciphertext position, which specifically includes:
the server sends q random integers C to the inquiring useri，
Figure BDA0002476024190000058
i-1, 2, …, q, wherein q is the sequence
Figure BDA0002476024190000059
The number of the elements in the Chinese character,
Figure BDA00024760241900000510
the querying user generates a key
Figure BDA00024760241900000511
Calculating q public keys and sending each public key to the server, wherein the public key corresponding to the ciphertext position is generated by a secret key k, and the rest public keys are generated according to the public key and the integer CiGenerating;
the server checks each public key to obtain a check result;
the server encrypts a path sequence by using the public key according to the check result and sends the path sequence to the inquiry user;
and the inquiry user decrypts the path ciphertext corresponding to the ciphertext position in the path sequence according to the key k, and can determine the optimal path of the social network by an accidental transmission mode.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
(1) the invention has high security, and all processes are realized by using an ElGamal encryption system. Even if the server has complete graph information such as vertex information and weight information, the server and external attackers cannot obtain any private information as long as there is no collusion between the user and the server. Meanwhile, the user cannot recover the user key of the connected user from the weight key obtained by the vector inner product. Therefore, the invention has high privacy protection safety.
(2) By means of an inadvertent transmission mode, due to the fact that discrete logarithm is difficult to assume, the server does not know the specific content of the path which the user wants to query, namely the specific position of the ciphertext b cannot be judged; because the server verifies the correctness of the public key in transmission, the user can only obtain a path, namely, the key required by other ciphertexts can not be forged or judged except k.
(3) The invention provides an optimal path matching method for protecting user privacy in a social network, which is characterized in that on the premise of ensuring the security, by using a data structure of bidirectional breadth-first search of a source point and a destination point, the operation speed of a server is high, the processing is efficient, and parameters in the process can be pre-generated so as to reduce the actual operation time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic diagram illustrating a system for protecting user privacy in a social network according to the present invention;
FIG. 2 is a schematic diagram of data transmission inside the system for protecting user privacy in the social network according to the present invention;
FIG. 3 is a flowchart of an optimal path matching method for protecting user privacy in a social network according to the present invention;
FIG. 4 is a flowchart of entity registration in a method according to an embodiment of the invention;
fig. 5 is a flow chart of an inadvertent transmission in a method according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a system for protecting user privacy in a social network and an optimal path matching method, which can ensure that propagation paths of any two vertexes of a large social network can be quickly inquired, the inquiry speed does not depend on the number of users, and the privacy protection safety is improved.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a schematic diagram illustrating a system for protecting user privacy in a social network according to the present invention. As shown in fig. 1, a system for protecting user privacy in a social network includes: trusted center 1, server 2, user 3. FIG. 2 is a schematic diagram of data transmission inside the system for protecting user privacy in the social network according to the present invention. As shown in fig. 2, the system includes a trusted center (TA), a server (CS) and a USER (USER). The trusted center 1 is used for generating system parameters and keys and sending the parameters to the server 2; the user 3 constructs the peak information and the weight information after registering, and sends the ciphertext information generated in the process of constructing the peak information and the weight information to the server 2, and the server 2 registersAnd then, the user 3 is used for providing the identity identifications of the user and the target user, and the server 2 is used for inquiring all propagation paths and corresponding weights, and providing the optimal path for the inquiring user in an inadvertent transmission mode. Suppose there are n USERs USER in the systemi(i ═ 1,2, …, n). The trusted center 1 distributes and calculates system parameters: identity ID, user key of user 3
Figure BDA0002476024190000077
Enquiry key (sk)1,sk2) Weight key f, query key (sk)1,sk2)，USERiUploading personal sensitive information to a server 2 through encryption, constructing a social graph G (V, E) composed of ciphertext through calculation and integration by the server 2, and providing Identification (ID) of a user and an Identity (ID) of a target user by a query user RU in a user 3s,IDt) The server 2 inquires out all the propagation paths
Figure BDA0002476024190000078
And corresponding weight value
Figure BDA0002476024190000079
Inquiring the user decryption weight sequence to obtain the subscript of the optimal propagation path, and transmitting OT through many-to-one carelessness1 qIn this way, the query user can obtain the optimal propagation path.
FIG. 3 is a flowchart of an optimal path matching method for protecting user privacy in a social network according to the present invention. As shown in fig. 3, an optimal path matching method for protecting user privacy in a social network includes:
step 101: the method comprises the following steps that the trusted center generates system parameters and a secret key and sends the system parameters to a server and a user, and specifically comprises the following steps:
the security parameter k is obtained.
According to the security parameters, calculating a master public key mpk and a master key msk of the encryption algorithm, wherein mpk is (g)msk,g,p)，
Figure BDA0002476024190000071
p is a large prime number and satisfies | p | ═ k,
Figure BDA0002476024190000072
represents [1, p-1 ]]In an arbitrary integer, g is
Figure BDA0002476024190000073
Selecting a random number R, wherein R < p/3.
Obtaining a key pair (sk)1,sk2) The key pair includes a first key sk1And a second key sk2Wherein
Figure BDA0002476024190000074
sk1+sk2＝msk mod(p-1)。
randomly acquiring n user key vectors
Figure BDA0002476024190000075
Wherein,
Figure BDA0002476024190000076
publishing parameters (mpk, g, p, R), i.e. public key for encryption, generator element, prime modulus information and system nonce information for the integration path.
Step 102: the registering of the user specifically includes:
a first registration request is sent.
The trusted center randomly selects an integer from the integer sequence as the identity ID of the current user according to the first registration requestiAnd combining the key vector
Figure BDA0002476024190000081
And returning to the current user.
Sending the ID to all users connected with the current useri。
The inquiring user obtains the IDiAnd then sends a second registration request.
The trusted center returns the first key sk to the inquiring user according to the second registration request1。
Step 103: the server performs registration, specifically including:
a third registration request is sent.
The trusted center returns the second key sk to the server according to the third registration request2。
Step 104: the user constructs vertex information and weight information, and sends ciphertext information generated in the process of constructing the vertex information and the weight information to the server, and the method specifically comprises the following steps:
obtaining the USERiThe attribute information of (1).
Binarizing the attribute information through one-hot coding to enable the value of only one bit in a binary value corresponding to each dimension attribute to be 1; the user can translate all discrete attributes of an individual into an attribute vector of length w
Figure BDA0002476024190000082
The USER USERiAccording to the key vector
Figure BDA0002476024190000083
Encrypting the attribute vector
Figure BDA0002476024190000084
Deriving vertex information v in a social networki(ii) a Wherein:
Figure BDA0002476024190000085
USER for each connected USERjThe current USER USERiAnd sending an application to the trusted center.
The trusted center returns a weight key according to the application, specifically
Figure BDA0002476024190000086
Calculating a weight ciphertext by using the encryption homomorphism property and the weight key, and sending the weight ciphertext to a server; wherein the weight value ciphertext
Figure BDA0002476024190000091
Calculated using the formula:
Figure BDA0002476024190000092
wherein ranki,jIs the USERiAnd USERjA predefined integer affinity value between.
And the server integrates the ciphertext to obtain weight information. The specific weight information is:
Figure BDA0002476024190000093
wherein the weight ei,jIs defined as:
Figure BDA0002476024190000094
step 105: the server is used for constructing a social graph according to the vertex information and the weight information; constructing a social graph G (V, E) according to the vertex information and the weight information, wherein: v ═ Vi|i∈[1,n]},E＝{Ei,j|i,j∈[1,n],USERiAnd USERjWith a connection }.
Step 106: and the inquiry user in the users uploads the starting end point identification to the server.
Step 107: the server generates a path sequence and a weight sequence according to the social graph and the start and end point identifier, and specifically includes:
determining a vertex v corresponding to a starting point identifier in the social graphsAnd is defined as a first layer set of starting vertices S1(ii) a Finding the vertex v corresponding to the terminal point identificationtAnd defined as a first layer termination vertex set T1。
Determining the first layer starting vertex set S1And the first layer termination vertex set T1Is denoted as vuWherein v isu∈{S1∩T1Querying a vertex v according to the social graphsAnd vuAnd vuAnd vtThe weights between them are respectively marked as Es,uAnd Eu,tA 1 is mixing Es,u·Eu,tAdded to the weight sequence
Figure BDA0002476024190000095
In, query vertex v simultaneouslyuCorresponding identification IDuWill IDuJoin to Path sequence
Figure BDA0002476024190000098
In (1).
Determining the first set of starting vertices S from the social graph1Each vertex v ofiAll connected vertices v ofjAnd is defined as the second layer initial vertex set S2At the same time vi→vjAdding to a set of prepositioned vertices P1。
Determining the second layer starting vertex set S2And the first layer termination vertex set T1Is updated by vuWherein v isu∈{S2∩T1At the same time in the set of front vertices P1In finding vuAnd is denoted by viSequentially querying vertexes v according to the social graphsAnd vi、viAnd vuAnd vuAnd vtThe weights between are respectively marked as Es,i、Ei,uAnd Eu,tA 1 is mixing Es,i·Ei,u·Eu,tAdded to the weight sequence
Figure BDA0002476024190000096
In, query vertex v simultaneouslyiAnd vuCorresponding identification IDiAnd IDuWill IDi·R+IDuJoin to Path sequence
Figure BDA0002476024190000097
In (1).
Determining the first-tier set of termination vertices T from the social graph1Each vertex v ofi′All connected vertices v ofj′And is defined as a second layer termination vertex set T2At the same time vi′→vj′Adding to a set of postpositional vertices P2。
Determining the second layer starting vertex set S2And the second layer termination vertex set T2Is updated by vuWherein v isu∈{S2∩T2At the same time in the set of front vertices P1In finding vuAnd is denoted by viAt said set of post-vertices P2In finding vuIs denoted by vi′Sequentially querying vertexes v according to the social graphsAnd vi、viAnd vu、vuAnd vi' and vi' and vuThe weights between are respectively marked as Es,i、Ei,u、Eu,i′And Ei′,tA 1 is mixing Es,i·Ei,u·Eu,i′·Ei′,tIs added to
Figure BDA00024760241900001016
In, query vertex v simultaneouslyi、vi' and vuCorresponding identification IDi、IDi′And IDuWill IDi·R2+IDu·R+IDi′Is added to
Figure BDA0002476024190000101
In (1), get the path sequence
Figure BDA0002476024190000102
Sum weight sequence
Figure BDA0002476024190000103
Step 108: the server sends the weight sequence to the querying user so that the querying user determines the ciphertext position, and the method specifically includes:
according to the second key sk2Decrypting the weight sequence
Figure BDA0002476024190000104
Each element of (a) obtains a first sequence of decryption weights
Figure BDA0002476024190000105
And sending the information to the inquiring user.
The inquiring user is according to the first key sk1Decrypting the first weight sequence
Figure BDA0002476024190000106
Each element of (2) to obtain a second decryption weight sequence
Figure BDA0002476024190000107
According to the second decryption weight sequence
Figure BDA0002476024190000108
And obtaining the ciphertext position.
Using the second key sk2Decipher the weight sequence
Figure BDA0002476024190000109
Each element of (1) to obtain
Figure BDA00024760241900001010
And sending to the inquiring user:
Figure BDA00024760241900001011
first key sk for inquiring user1Decipher the weight sequence
Figure BDA00024760241900001012
Each of (1)Elements:
Figure BDA00024760241900001013
i.e. the final decrypted w ″iFor the total weight of each path, RU may sort out the index of the minimum value, i.e. the ciphertext position, by a sorting algorithm, e.g. bubble algorithm
Figure BDA00024760241900001014
Step 109: the query user determines an optimal path from the path sequence by adopting an inadvertent transmission mode according to the ciphertext position, and the method specifically comprises the following steps:
the server sends q random integers C to the inquiring useri，
Figure BDA00024760241900001015
Wherein q is a sequence
Figure BDA0002476024190000111
Number of elements in, i.e.
Figure BDA0002476024190000112
The querying user computes q public keys and combines each of the public keys β1,β2,…,βqSending the data to the server; wherein random numbers are selected
Figure BDA0002476024190000113
Computing the b-th said public key βbAnd the other public keys are calculated by the public keys of the adjacent subscripts and the random integer step by step in two directions to form a chain structure:
βi＝Ci/βi+1mod p,i＝1,2,…,b-1
βb＝gkmod p
βj＝Cj-1/βj-1mod p,j＝b+1,b+2,…,q
said server checking each of said public keys, i.e. checking Ci＝βi·βi+1mod p, get the inspection result.
The server encrypts the path sequence by the public key according to the check result and sends the path sequence to the inquiring user, β is used by the serveriEncrypt miAnd sending to the RU:
Figure BDA0002476024190000114
and the inquiry user decrypts the path ciphertext corresponding to the ciphertext position in the path sequence according to the key k, and can determine the optimal path of the social network by an accidental transmission mode. Namely, the inquiry user decrypts the b-th ciphertext c by using the key kbObtaining an optimal path
Figure BDA0002476024190000115
And finally obtaining each vertex identification ID in the optimal path through iterative computationi＝(mb-(mbmod R))/R。
Example 1:
the invention provides an optimal path matching method for protecting user privacy in a social network, which comprises the following steps:
step 1: and generating system parameters.
Step 1.1: the trusted center (TA) selects the security parameter k and calculates the master public key (mpk ═ g) of the ElGamal encryption algorithmmskG, p) and a master key
Figure BDA0002476024190000116
Wherein p is a large prime number and satisfies | p | ═ k, and g is
Figure BDA0002476024190000117
A generator of (2). A random number R is selected, where R < p/3.
Step 1.2: TA selects a pair of keys (sk)1,sk2) Wherein
Figure BDA0002476024190000118
Make sk1+sk2＝msk mod(p-1)。
Step 1.3: TA random selection of n user key vectors
Figure BDA0002476024190000119
Step 1.4: TA issues parameters (mpk, g, p, R).
The entity registration process relates to fig. 4.
Step 2: and (4) registering the entity.
Step 2.1: USERi(i ═ 1,2, …, n) is registered.
Step 2.1.1: USERi(i ═ 1,2, …, n) sends a registration request.
Step 2.1.2: TA randomly selects an integer from the sequence of integers {1,2, …, n } as the user's IDiAnd will be
Figure BDA0002476024190000121
And returning to the user.
Step 2.1.3: USERiSending his own ID to all connected usersi。
Step 2.1.4: the querying user RU sends a registration request.
Step 2.1.5: TA Return Key sk to RU1。
Step 2.2: the server CS registers.
Step 2.2.1: the server CS sends a registration request.
Step 2.2.2: TA returns the secret key sk to CS2。
And step 3: and building a social graph.
Step 3.1: and constructing vertex information.
Step 3.1.1: USERiAnd (4) binarizing the attribute information of the user by one-hot coding, namely, the value of only one bit in the binary value corresponding to each dimension attribute is 1. Wherein:
sex is male: sexi10; sex is female: sexi＝01。
Age 0-20: age (age)i100; age 21-50: age (age)i010; age 50 or above: age (age)i＝001。
So that all discrete attributes of the user are converted into an attribute vector with each element being a binary value
Figure BDA0002476024190000122
Where w represents the length of the user's attribute vector.
Step 3.1.2: USERiUsing secret keys
Figure BDA0002476024190000123
Encryption attribute vector
Figure BDA0002476024190000124
Form a vertex
Figure BDA0002476024190000125
Figure BDA0002476024190000126
Figure BDA0002476024190000127
Step 3.2: and constructing weight information.
Step 3.2.1: USER for each connected USERjUSERiSending an application to the TA
Figure BDA0002476024190000131
Step 3.2.2: TA query IDjCorresponding user key vector
Figure BDA0002476024190000132
Calculating and returning weight value key fj,i：
Figure BDA0002476024190000133
Step 3.2.3: USERiComputing weight ciphertext by utilizing ElGamal homomorphism property
Figure BDA0002476024190000134
And sends it to the server CS:
Figure BDA0002476024190000135
wherein ranki,jIs the USERiAnd USERjA predefined integer affinity value between.
Step 3.2.4: the CS integrates the ciphertext and forms a weight as:
Figure BDA0002476024190000136
wherein the weight ei,jIs defined as:
Figure BDA0002476024190000137
this indicates that the closer the two users are, the faster the message is propagated, so that the weight between the users is smaller.
Step 3.3: from the vertices and edges, a social graph G ═ V, E can be constructed.
Wherein: v ═ Vi|i∈[1,n]},E＝{Ei,j|i,j∈[1,n],USERiAnd USERjWith a connection }.
And 4, step 4: and (6) path query.
Step 4.1: inquiring the Identification (ID) corresponding to the starting and ending point uploaded by the RU of the users,IDt) To the server CS.
Step 4.2: CS generation path sequence
Figure BDA0002476024190000138
Sum weight sequence
Figure BDA0002476024190000139
Step 4.2.1: CS is in graph GFind the starting point vsAnd is defined as a first layer set of starting vertices S1(ii) a Finding the end point vtAnd defined as a first layer termination vertex set T1. Wherein v is not includedsAnd vt。
Step 4.2.2: CS sorting out S1And T1Common vertex v in (1)u∈{S1∩T1V, query vertex vsAnd vuAnd vuAnd vtIn between, and Es,u·Eu,tIs added to
Figure BDA0002476024190000141
In (1), v isuCorresponding identification IDuIs added to
Figure BDA0002476024190000142
In (1).
Wherein:
Figure BDA0002476024190000143
the homomorphism property of the ElGamal encryption system is used to aggregate the weights of the two connected edges together.
Step 4.2.3: CS finds S in graph G1Each vertex v ofiAll connected vertices v ofjAnd is defined as the second layer initial vertex set S2At the same time vi→vjAdding to a set of prepositioned vertices P1(ii) a Wherein v is not includedsAnd vt。
Step 4.2.4: CS sorting out S2And T1Common vertex v in (1)u∈{S2∩T1At P1In finding vuIs v as the leading vertexiQuerying the vertex vsAnd vi、viAnd vuAnd vuAnd vtIn between, and Es,i·Ei,u·Eu,tIs added to
Figure BDA0002476024190000144
In, query vertex v simultaneouslyiAnd vuCorresponding identification IDiAnd IDuWill IDi·R+IDuIs added to
Figure BDA0002476024190000145
In (1).
Wherein:
Figure BDA0002476024190000146
step 4.2.5: CS finds T in graph G1Each vertex v ofi′All connected vertices v ofj′And is defined as a second layer termination vertex set T2At the same time vi′→vj′Adding to a set of postpositional vertices P2(ii) a Wherein v is not includedsAnd vt。
Step 4.2.6: CS sorting out S2And T2Common vertex v in (1)u∈{S2∩T2At P1In finding vuIs v as the leading vertexiAt P2In finding vuHas a post-vertex of vi′Sequentially querying vertexes v according to the social graphsAnd vi、viAnd vu、vuAnd vi' and vi' and vuA weight value of E betweens,i·Ei,u·Eu,i′·Ei′,tIs added to
Figure BDA0002476024190000151
In, query vertex v simultaneouslyi、vi' and vuCorresponding identification IDi、IDi′And IDuWill IDi·R2+IDu·R+IDi′Is added to
Figure BDA0002476024190000152
In (1).
Wherein:
Figure BDA0002476024190000153
step 4.3: RU picks out the index b of the optimal path in the path sequence.
Step 4.3.1: secret key sk for CS2Decipher the weight sequence
Figure BDA0002476024190000154
Each element of (1) to obtain
Figure BDA0002476024190000155
And sending to the RU:
Figure BDA0002476024190000156
step 4.3.2: RU Key sk1Decipher the weight sequence
Figure BDA0002476024190000157
Each element of (a):
Figure BDA0002476024190000158
i.e. the final decrypted w ″iFor the total weight of each path, through a sorting algorithm, such as a bubble algorithm, the RU may select the subscript corresponding to the minimum value as
Figure BDA0002476024190000159
The process of inadvertent transmission relates to fig. 5.
Step 4.4: RU inadvertently acquires optimal path mb。
Step 4.4.1: CS sends q random integers to RU
Figure BDA00024760241900001510
Wherein q is a sequence
Figure BDA00024760241900001511
The number of the elements in the Chinese character,namely, it is
Figure BDA00024760241900001512
Step 4.4.2: RU selecting random number
Figure BDA00024760241900001513
Calculation β1,β2,…,βqAnd sending to the CS:
βi＝Ci/βi+1mod p,i＝1,2,…,b-1
βb＝gkmod p
βj＝Cj-1/βj-1mod p,j＝b+1,b+2,…,q
step 4.4.3: CS examination Ci＝βi·βi+1mod p。
Step 4.4.4: β for CSiEncrypt miAnd sending to the RU:
Figure BDA00024760241900001514
step 4.4.5: RU decrypts the b-th ciphertext c by using the key kbObtaining an optimal path
Figure BDA0002476024190000161
And finally obtaining each vertex identification ID in the optimal path through iterative computationi＝(mb-(mbmod R))/R。
The invention is based on ElGamal homomorphic encryption and inadvertent transmission OT1 qThe method realizes an optimal path matching scheme for protecting the privacy of the user in the social network, and the scheme realizes the resistance to external attack and internal attack.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (9)

1. A system for protecting user privacy in a social network, comprising: the system comprises a trusted center, a server and a user, wherein the trusted center is used for generating system parameters and a secret key and sending the parameters to the server; the method comprises the steps that after the user registers, vertex information and weight information are constructed, ciphertext information generated in the process of constructing the vertex information and the weight information is sent to a server, the server is used for constructing a social graph according to the vertex information and the weight information after registering, a query user in the user is used for providing identification marks of the user and a target user, the server is used for querying all propagation paths and corresponding weights, and an optimal path is provided for the query user through an inadvertent transmission mode.
2. An optimal path matching method for protecting user privacy in a social network is characterized by comprising the following steps:
the trusted center generates system parameters and a secret key and sends the system parameters to the server and the user;
the user registers;
the server registers;
the user constructs vertex information and weight information and sends ciphertext information generated in the process of constructing the vertex information and the weight information to the server;
the server is used for constructing a social graph according to the vertex information and the weight information;
a query user in the users uploads a starting end point identifier to the server;
the server generates a path sequence and a weight sequence according to the social graph and the starting and ending point identification;
the server sends the weight value sequence to the inquiry user so that the inquiry user can determine the ciphertext position;
and the inquiry user determines an optimal path from the path sequence by adopting an inadvertent transmission mode according to the ciphertext position.
3. The optimal path matching method for protecting user privacy in a social network according to claim 1, wherein the trust center generates system parameters and a secret key and sends the system parameters to the server and the user, and specifically comprises:
acquiring a safety parameter kappa;
according to the security parameters, calculating a master public key mpk and a master key msk of the encryption algorithm, wherein mpk is (g)msk,g,p)，
Figure FDA0002476024180000011
p is a large prime number and satisfies | p | ═ k,
Figure FDA0002476024180000012
is [1, p-1 ]]In an arbitrary integer, g is
Figure FDA0002476024180000013
Selecting a random number R, wherein R < p/3;
obtaining a key pair (sk)1,sk2) The key pair includes a first key sk1And a second key sk2Wherein, sk1,
Figure FDA0002476024180000021
sk1+sk2＝msk mod(p-1)；
Randomly acquiring n user key vectors
Figure FDA0002476024180000022
Wherein,
Figure FDA0002476024180000023
and issuing parameter information, wherein the parameter information comprises the master public key, the generator, the prime number and the random number.
4. The optimal path matching method for protecting user privacy in a social network according to claim 3, wherein the user registration specifically includes:
sending a first registration request;
the trusted center randomly selects an integer from the integer sequence as the identity ID of the current user according to the first registration requestiAnd combining the key vector
Figure FDA0002476024180000024
Returning to the current user;
sending the ID to all users connected with the current useri；
The inquiring user obtains the IDiThen sending a second registration request;
the trusted center returns the first key sk to the inquiring user according to the second registration request1。
5. The optimal path matching method for protecting user privacy in a social network according to claim 3, wherein the server performs registration, specifically comprising:
sending a third registration request;
the trusted center returns the second key sk to the server according to the third registration request2。
6. The optimal path matching method for protecting user privacy in a social network according to claim 3, wherein the user constructs vertex information and weight information, and sends ciphertext information generated in the process of constructing the vertex information and the weight information to the server, specifically comprising:
obtaining the USERiAttribute information of (2);
binarizing the attribute information through one-hot coding to enable the value of only one bit in a binary value corresponding to each dimension attribute to be 1; the user can translate all discrete attributes of an individual into an attribute vector of length w
Figure FDA0002476024180000025
The USER USERiAccording to the key vector
Figure FDA0002476024180000026
Encrypting the attribute vector
Figure FDA0002476024180000027
Deriving vertex information v in a social networki；
USER for each connected USERjThe current USER USERiSending an application to the trusted center;
the trusted center returns a weight key according to the application;
calculating a weight ciphertext by using the encryption homomorphism property and the weight key, and sending the weight ciphertext to a server;
and the server integrates the ciphertext to obtain weight information.
7. The method as claimed in claim 3, wherein the server generates a path sequence according to the social graph and the start and end point id
Figure FDA0002476024180000031
Sum weight sequence
Figure FDA0002476024180000032
The method specifically comprises the following steps:
determining a vertex v corresponding to a starting point identifier in the social graphsAnd is defined as a first layer set of starting vertices S1(ii) a Finding the vertex v corresponding to the terminal point identificationtAnd defined as a first layer termination vertex set T1；
Determining the first layer starting vertex set S1And the first layer termination vertex set T1Is denoted by vuWherein v isu∈{S1∩T1Querying a vertex v according to the social graphsAnd vuAnd vuAnd vtThe weights between them are respectively marked as Es,uAnd Eu,tWill Es,u·Eu,tIs added to
Figure FDA0002476024180000033
In, query vertex v simultaneouslyuCorresponding identification IDuWill IDuIs added to
Figure FDA0002476024180000034
Performing the following steps;
determining the first set of starting vertices S from the social graph1Each vertex v ofiAll connected vertices v ofjAnd is defined as the second layer initial vertex set S2At the same time vi→vjAdding to a set of prepositioned vertices P1；
Determining the second layer starting vertex set S2And the first layer termination vertex set T1Is updated by vuWherein v isu∈{S2∩T1At the same time in the set of front vertices P1In finding vuAnd is denoted by viSequentially querying vertexes v according to the social graphsAnd vi、viAnd vuAnd vuAnd vtThe weights between are respectively marked as Es,i、Ei,uAnd Eu,tA 1 is mixing Es,i·Ei,u·Eu,tIs added to
Figure FDA0002476024180000035
In, query vertex v simultaneouslyiAnd vuCorresponding identification IDiAnd IDuWill IDi·R+IDuIs added to
Figure FDA0002476024180000036
Performing the following steps;
determining the first-tier set of termination vertices T from the social graph1Each vertex v ofi′All connected vertices v ofj′And is defined as a second layer termination vertex set T2At the same time vi′→vj′Adding to a set of postpositional vertices P2；
Determining the second layer starting vertex set S2And the second layer termination vertex set T2Is updated by vuWherein v isu∈{S2∩T2At the same time in the set of front vertices P1In finding vuAnd is denoted by viAt said set of post-vertices P2In finding vuIs denoted by vi′Sequentially querying vertexes v according to the social graphsAnd vi、viAnd vu、vuAnd vi′And vi′And vuThe weights between are respectively marked as Es,i、Ei,u、Eu,i′And Ei′,tA 1 is mixing Es,i·Ei,u·Eu,i′·Ei′,tIs added to
Figure FDA0002476024180000037
In, query vertex v simultaneouslyi、vi′And vuCorresponding identification IDi、IDi′And IDuWill IDi·R2+IDu·R+IDi′Is added to
Figure FDA0002476024180000038
To getSequence of arrival paths
Figure FDA0002476024180000041
Sum weight sequence
Figure FDA0002476024180000042
8. The optimal path matching method for protecting user privacy in a social network according to claim 3, wherein the server sends the weight sequence to the querying user so that the querying user determines a ciphertext position, specifically comprising:
according to the second key sk2Decrypting the weight sequence
Figure FDA0002476024180000043
Each element of (a) obtains a first sequence of decryption weights
Figure FDA0002476024180000044
And sending to the inquiring user;
the inquiring user is according to the first key sk1Decrypting the first weight sequence
Figure FDA0002476024180000045
Each element of (2) to obtain a second decryption weight sequence
Figure FDA0002476024180000046
According to the second decryption weight sequence
Figure FDA0002476024180000047
And obtaining the ciphertext position.
9. The optimal path matching method for protecting the privacy of the user in the social network according to claim 3, wherein the query user determines the optimal path from the path sequence by an inadvertent transmission method according to the ciphertext position, specifically comprising:
the server sends q random integers C to the inquiring useri，
Figure FDA0002476024180000048
Wherein q is a sequence
Figure FDA0002476024180000049
The number of the elements in the Chinese character,
Figure FDA00024760241800000410
the querying user generates a key
Figure FDA00024760241800000411
Calculating q public keys and sending each public key to the server, wherein the public key corresponding to the ciphertext position is generated by a secret key k, and the rest public keys are generated according to the public key and the integer CiGenerating;
the server checks each public key to obtain a check result;
the server encrypts a path sequence by using the public key according to the check result and sends the path sequence to the inquiry user;
and the inquiry user decrypts the path ciphertext corresponding to the ciphertext position in the path sequence according to the key k, and can determine the optimal path of the social network by an accidental transmission mode.
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