CN114579997A - Encrypted social network graph node intimacy calculation method - Google Patents

Encrypted social network graph node intimacy calculation method Download PDF

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CN114579997A
CN114579997A CN202210214247.8A CN202210214247A CN114579997A CN 114579997 A CN114579997 A CN 114579997A CN 202210214247 A CN202210214247 A CN 202210214247A CN 114579997 A CN114579997 A CN 114579997A
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CN114579997B (en
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陈兰香
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
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Abstract

The invention relates to a method for calculating node affinity of an encrypted social network graph. The data of the social network graph is encrypted by using a structured encryption technology, and a secret set intersection protocol is designed by using a bloom filter and a chaotic bloom filter, so that the intimacy of the encrypted nodes of the social network graph can be calculated.

Description

Encrypted social network graph node intimacy calculation method
Technical Field
The invention belongs to the field of network space security, safety and confidentiality, and particularly relates to a method for calculating the node intimacy of an encrypted social network graph.
Background
The social network graph comprises node attributes and relations among nodes, is a complex data structure, and the traditional searchable encryption technology mainly aims at text data and is not suitable for the social network graph. The structured encryption technology can realize ciphertext retrieval of a complex data structure and can be used for encrypting the social network diagram, but the structured encryption scheme can only realize retrieval of encrypted data and cannot perform calculation and statistical analysis on the encrypted social network diagram.
Disclosure of Invention
The invention aims to provide a method for calculating the intimacy of nodes of an encrypted social network graph.
In order to achieve the purpose, the technical scheme of the invention is as follows: a method for calculating intimacy of nodes of an encrypted social network diagram is characterized in that structured encryption technology is used for encrypting data of the social network diagram, and a secret set intersection protocol is designed by using a bloom filter and a chaotic bloom filter, so that the intimacy of the nodes of the encrypted social network diagram is calculated.
In an embodiment of the present invention, the method is specifically implemented as follows:
firstly, constructing a neighbor node table of each node in a social network graph; then encrypting the constructed neighbor node table, namely replacing each node item by using a pseudo-random replacement pi to obtain an encrypted neighbor node linked list; then calculate vi＝PK1(i) As an entry of an encrypted neighbor node linked list for node i, L is used for the set of elements in the linked listiTo identify;
constructing a keyword index table for node attributes, namely keywords in the social network graph, adding nodes containing corresponding keywords and neighbor node linked lists thereof into an index, using ciphertext of the keywords as an entry of an index item, and adding a random mask FK2(w); then calculating the ciphertext P of the keyword wK1(w) as an entry for the index entry;
the social network graph data owner stores the encrypted neighbor node linked list and the encrypted keyword index table to the cloud server; when a user wants to inquire a node which is closest to the node a and has the attribute w, a query request is sent to a social network graph data owner, the social network graph data owner determines whether to authorize the user query according to an access control strategy, and if the user query is authorized, a query Token (Token ═ tau, v) is generateda) Wherein τ ═ PK1(w),FK2(w))，va＝PK1(ma) Wherein a represents a node, is a digital identifier and has uniqueness, and m is a function of a nodeaRepresenting node names, e.g. user names in a social network, and sending to a professorRight users;
the authorized user sends a Token request query to the cloud server, and the cloud server utilizes P in the TokenK1(w) find the keyword index, calculate (pi [ i ]],vi)＝τ⊕FK2(w), the results are denoted by vI(ii) a Cloud Server according to vIObtaining a neighbor node set L of nodes containing the keyword wI *Then according to vaGet node maSet of neighbor nodes La *(ii) a The cloud server runs a secret set transaction protocol (max-PSI protocol) and returns to the node maNode v with the most neighboring nodesmaxAnd the cipher text c of the node is processedj(j＝π[max]) Returning to the authorized user; obtaining m by decryption of authorized user by using keyjNamely, the plaintext node satisfies the condition.
In an embodiment of the present invention, the operation process of the secret set transaction protocol, i.e. the max-PSI protocol, is as follows:
let GBFaIs a neighbor node array L of the cloud server according to the interested node aaThe generated chaotic bloom filter has a number of groups L of | R (w) |x(x epsilon R (w)) all generate corresponding bloom filters BFx(ii) a Let countx(x epsilon R (w)) is the number of the same neighbor nodes of the node x and the node a, and is BFxAnd GBFaCounting the same elements in the sequence; the construction of the max-PSI protocol comprises the following stages:
(1) setup: the user randomly selects l random numbers ri(i is more than or equal to 0 and less than or equal to l) to form a character string array A, the character string array A is sent to a cloud server, and count is carried outx(x ∈ R (w)) all initialized to 0;
(2) computing Set interaction: for each BFxIf BFx[i]GBF is selected as 1a[i](ii) a If BFx[i]When equal to 0, select A [ i]Finally, get GBFa∩xI.e. BFxAnd GBFaThe intersection of (a);
(3) output: for LxGBF is calculated for each element b stored in (x ∈ R (w)))a∩x[h1(b)]⊕…⊕GBFa∩x[hk(b)]If the result is b, then the data is processed,then countx Adding 1, otherwise countxThe change is not changed; repeating the operation to calculate all the count values, comparing to obtain the maximum count value, and comparing the corresponding vxI.e. vmaxAnd returning the data to the cloud server.
Compared with the prior art, the invention has the following beneficial effects:
(1) the encryption query of the social network graph data is realized by using a structured encryption technology, but the structured encryption scheme can only realize the retrieval of encrypted data and cannot calculate and statistically analyze the encrypted social network graph. The invention provides a privacy computing protocol max-PSI which is used for realizing statistical analysis on the structured encrypted social network diagram data.
(2) The max-PSI protocol utilizes a bloom filter and a chaotic bloom filter to achieve secure set intersection, so that the affinity of the encrypted social network graph nodes can be calculated.
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FIG. 1 is a schematic view of the overall scheme of the present invention.
FIG. 2 is a bloom filter and chaotic bloom filter after adding two elements, wherein (a) the bloom filter; (b) chaotic bloom filters.
FIG. 3 is a diagram of entity relationships.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
The invention discloses a method for calculating the intimacy of nodes of an encrypted social network graph.
The following is a specific implementation process of the present invention.
The overall scheme of the present invention is shown in fig. 1, in which the social network diagram is only an example. Firstly, constructing a neighbor node table of each node in the social network graph, for example, the neighbor node of the node 1 is {2, 3, 4}, the neighbor node of the node 2 is {1, 6}, the neighbor node of the node 3 is {1, 4, 5, 6}, and constructing the neighbor node of each node in the same wayTable (7). The invention needs to protect the security of the nodes and data in the social network graph, so all the information of the nodes and the node attributes is stored in an encrypted manner. Therefore, the constructed plaintext neighbor node table is encrypted, namely each node item is replaced by using the pseudo-random replacement pi to obtain the encrypted neighbor node table. Then calculate vi＝PK1(i) As an entry of an encrypted neighbor node linked list for node i, L is used for the set of elements in the linked listiTo identify.
For node attribute, i.e. keyword { w in graph1,w2,w3,w4And constructing a keyword index table, adding the node containing the keyword and the neighbor node linked list thereof into the index, and using the ciphertext of the keyword as an entry of the index item. For the keyword w1The node containing the key has {1, 2, 5}, and thus will { (π [1 ] }],v1),(π[2],v2),(π[5],v5) Adding random mask F to protect the informationK2(w1). Then calculate the keyword w1Ciphertext P ofK1(w1) As an entry for the index entry.
And the social network graph data owner stores the encrypted neighbor node linked list and the keyword index table to the cloud server. When a user wants to inquire a node which is closest to the node a and has the attribute w, a query request is sent to a social network graph data owner, the graph data owner determines whether to authorize the user query according to an access control strategy, and if the user query is authorized, a query Token (Token ═ tau, v) is generateda) Wherein τ ═ PK1(w),FK2(w))，va＝PK1(ma) Wherein a represents a node, is a digital identifier and has uniqueness, and m is a unique identifieraThe node name, such as the name of the user in the social network, is represented and sent to the authorized user.
The authorized user sends a Token request query to the cloud server, and the cloud server utilizes P in the TokenK1(w) find the keyword index, calculate (pi [ i ]],vi)＝τ⊕FK2(w), the results are denoted by vI. Cloud Server according to vIGet the section containing the keyword wSet of neighbor nodes L of a pointI *Then according to vaGet node maSet of neighbor nodes La *. The cloud server runs a max-PSI protocol and returns a node v with the most neighbor nodes to the node amaxAnd the cipher text c of the node is processedj(j＝π[max]) And returning to the user. Obtaining m by decryption of authorized user by using keyjI.e. the plaintext node that satisfies the condition.
Before introducing the max-PSI protocol, the construction of a bloom filter and a chaotic bloom filter needs to be introduced.
A Bloom Filter (BF) consists of a binary vector and a series of random hash functions. The method can be used for searching whether a keyword is in a certain keyword set, and the storage overhead and efficiency of the query are superior to those of a general structure. The bloom filter can be regarded as a vector with x bits, if there exists a set of m and k mutually independent hash functions h1,……,hkThe collection elements can be mapped into the bloom filter by a hash function. The bloom filters all have an initial value of 0, and the hash function mapped address { BF [ h ]1(mi)],……,BF[hk(mi)]The corresponding position of the structure is 1, and the structure is shown in fig. 2 (a).
A chaotic Bloom Filter (GBF) may better resolve the problem of Bloom Filter hash collisions, causing it to have negligible false positives. The chaotic bloom filter formally converts a bit array in the bloom filter into a character string array, wherein the length of each character string in the array is a safety parameter lambda1We can obtain the required security by adjusting this parameter.
Let there be a set of m and k mutually independent hash functions h1,……,hkWhen inserting elements, the elements m are sequentially hashed by k hash functionsiMapping to k positions of the character string array, taking the first hash address as inm, and writing a length of lambda if the following hash address is empty1Otherwise, the random string remains unchanged. Then theExclusive OR is carried out on character strings of all hash addresses except the hash address inm, and the exclusive OR result and m are comparediXOR, writing the obtained value into inm position of GBF, i.e. assigning XOR value to GBF [ inm ]]. After all elements have been inserted, all unassigned positions are written into a random string.
For a chaotic bloom filter, if y is not in the set m, then the probability that the k string XOR result equals y is for λ1Can be ignored. Meanwhile, the probability of collision is also a negligible function with respect to the number k of hash functions. The structure of the chaotic bloom filter is shown in FIG. 2(b), where { r }11,……,h1k}，{r21,……,h2kBoth are random strings because h1(m1) Position is empty, so s1＝r12⊕r13⊕…⊕r1k⊕m1(ii) a At the same time, because h1(m2) Position is not empty, and position h2(m2) Is empty, so s2＝r1k⊕r23⊕…⊕r2k⊕m2. When element m is to be judgedxWhether in the set, calculate h1(mx)⊕h2(mx)⊕…⊕hk(mx) Whether or not it is mxIf so, the data is in the set, otherwise, the data is not in the set.
The max-PSI protocol runs as follows:
let GBFaIs a neighbor node array L of the cloud server according to the node a in which we are interestedaThe generated chaotic bloom filter has a number of groups L of | R (w) |x(x ∈ R (w)) all generate corresponding bloom filters BFx. Let countx(x epsilon R (w)) is the number of the same neighbor nodes of the node x and the node a, and is BFxAnd GBFaThe count of the same elements in the sequence. The construction of the max-PSI protocol comprises the following stages:
(3) setup the user randomly selects l random numbers ri(i is more than or equal to 0 and less than or equal to l) to form a character string array A, the character string array A is sent to a cloud server, and count is carried outx(x ∈ R (w)) was all initialized to 0.
(4) Computing Set interaction for each BFxIf BFx[i]GBF is selected as 1a[i](ii) a If BFx[i]When equal to 0, select A [ i]Finally, get GBFa∩x(BFxAnd GBFaThe intersection of (d).
(3) Output for LxGBF is calculated for each element b stored in (x ∈ R (w)))a∩x[h1(b)]⊕…⊕GBFa∩x[hk(b)]If the result is b, countxAdding 1, otherwise countxAnd is not changed. Repeating the operation to calculate all the count values, comparing to obtain the maximum count value, and comparing the corresponding vxI.e. vmaxAnd returning the data to the cloud server.
Examples
Taking FIG. 1 as an example, if the user wants to inquire about the owned keyword w1Meanwhile, the node closest to the node 4 first acquires Token ═ τ, v, from the graph data owner4) Wherein τ ═ PK1(w1),FK2(w1))，v4＝PK1(m4)。
By PK1(w1) Find w from FIG. 1(c)1Signed encrypted data pair, using FK2(w1) XOR-ing with the encrypted data to obtain semi-private data { v }1,v2,v5}. Obtaining { L ] from FIG. 1(b)1,L2,L5}. Then use L1,L2,L5Generated BF1,BF2,BF5And GBF4Running max-PSI privacy computation protocol, v can be obtained5So the node with the highest affinity to node 4 is node 5 and the corresponding ciphertext c will be generatedπ[5]Returning to the user, and decrypting the plaintext m by the user by using the secret key5. The overall process is shown in figure 3.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.

Claims (3)

1. A method for calculating the intimacy of nodes of an encrypted social network graph is characterized in that the social network graph data are encrypted by using a structured encryption technology, and a secret set intersection protocol is designed by using a bloom filter and a chaotic bloom filter, so that the intimacy of the nodes of the encrypted social network graph is calculated.
2. The method for calculating node affinity of the encrypted social network graph according to claim 1, wherein the method is implemented as follows:
firstly, constructing a neighbor node table of each node in the social network graph; then encrypting the constructed neighbor node table, namely replacing each node item by using a pseudo-random replacement pi to obtain an encrypted neighbor node linked list; then calculate vi＝PK1(i) As an entry of an encrypted neighbor node linked list for node i, L is used for the set of elements in the linked listiTo identify;
constructing a keyword index table for node attributes, namely keywords in the social network graph, adding nodes containing corresponding keywords and neighbor node linked lists thereof into an index, using ciphertext of the keywords as an entry of an index item, and adding a random mask FK2(w); then calculating the ciphertext P of the keyword wK1(w) as an entry for the index entry;
the social network graph data owner stores the encrypted neighbor node linked list and the encrypted keyword index table to the cloud server; when a user wants to inquire a node which is closest to the node a and has the attribute w, a query request is sent to a social network graph data owner, the social network graph data owner determines whether to authorize the user query according to an access control strategy, and if the user query is authorized, a query Token (Token ═ tau, v) is generateda) Wherein τ ═ PK1(w),FK2(w))，va＝PK1(ma) Wherein a represents a node, is a digital identifier and has uniqueness, and m is a function of a nodeaRepresenting the node name and sending to an authorized user;
the authorized user sends a Token request query to the cloud server, and the cloud server utilizes P in the TokenK1(w) find the keyword index, calculate (pi [ i ]],vi)＝τ⊕FK2(w), the results are denoted by vI(ii) a Cloud Server according to vIObtaining a neighbor node set L of nodes containing the keyword wI *Then according to vaObtaining a neighbor node set L of the node aa *(ii) a The cloud server runs a secret set transaction protocol (max-PSI protocol) and returns a node v with the most neighbor nodes with the node amaxAnd the cipher text c of the node is processedj(j＝π[max]) Returning to the authorized user; obtaining m by decryption of authorized user by using keyjNamely, the plaintext node satisfies the condition.
3. The method of claim 2, wherein the secure set intersection protocol (max-PSI) is operated as follows:
let GBFaIs a neighbor node array L of the cloud server according to the interested node aaThe generated chaotic bloom Filter for the | R (w) | number group Lx(x ∈ R (w)) all generate corresponding bloom filters BFx(ii) a Let countx(x ∈ R (w)) is the number of the same neighbor nodes of the node x and the node a, and is BFxAnd GBFaCounting the same elements in the sequence; the construction of the max-PSI protocol comprises the following stages:
(1) setup: the user randomly selects l random numbers ri(i is more than or equal to 0 and less than or equal to l) to form a character string array A, the character string array A is sent to a cloud server, and count is carried outx(x ∈ R (w)) all initialized to 0;
(2) computing Set interaction: for each BFxIf BFx[i]GBF is selected as 1a[i](ii) a If BFx[i]When equal to 0, select A [ i]Finally, get GBFa∩xI.e. BFxAnd GBFaThe intersection of (a);
(3) output: for LxGBF is calculated for each element b stored in (x ∈ R (w))a∩x[h1(b)]⊕…⊕GBFa∩x[hk(b)]If the result is b, countxAdding 1, otherwise countxThe change is not changed; repeating the operation to calculate all the count values, comparing to obtain the maximum count value, and comparing the corresponding vxI.e. vmaxAnd returning the data to the cloud server.
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