CN115422432A - Dynamic searchable encryption method for massive high-dimensional medical data - Google Patents
Dynamic searchable encryption method for massive high-dimensional medical data Download PDFInfo
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Abstract
The invention relates to the technical field of encrypted data query, in particular to a dynamic searchable encryption method for massive high-dimensional medical data; the medical center encrypts an electronic medical record data set to obtain ciphertext data, and generates a corresponding index vector for each electronic medical record based on a constructed attribute value set and an attribute hierarchical tree; constructing an index tree according to the index vector and encrypting to obtain an encrypted index tree; uploading the ciphertext data and the encrypted index tree to a medical cloud server; the data user obtains a search trapdoor from the medical center and feeds the search trapdoor back to the medical cloud server, and the medical cloud server performs matching on the encrypted index tree according to the search trapdoor to obtain a matched electronic medical record ciphertext; meanwhile, the medical center dynamically updates the encrypted data and the encrypted index in the medical cloud server; the invention reduces the dimensionality of the index vector through the attribute hierarchical structure, thereby not only reducing the calculated amount and the communication overhead, but also realizing the range retrieval of the attribute value.
Description
Technical Field
The invention relates to the technical field of data encryption query, in particular to a dynamic searchable encryption method for massive high-dimensional medical data.
Background
In recent years, with the development of medical informatization, many paper medical records are converted into Electronic Medical Records (EMRs), so that a large amount of electronic medical record data is generated. For each medical center, how to realize safe and effective management, storage and retrieval of massive EMRs becomes a problem which needs to be solved urgently. In order to ensure the safety of data, the important measures are taken to encrypt and store the electronic medical record data and then search and inquire the encrypted and stored electronic medical record data. Many existing keyword-based search techniques are widely applied to plaintext data, and cannot be directly applied to ciphertext data. Searchable encryption technology provides a method for keyword retrieval on ciphertext.
Searchable encryption technology is one solution that enables users to search for encrypted data. At present, most of searchable encryption schemes supporting multiple keywords only support simple keyword equal queries, and multiple keyword queries supporting keyword range queries and subset queries are generally asymmetric searchable encryption based on complex algebraic structures, so that the calculation cost is high. Another class of multi-keyword searchable encryption schemes is directed to file ranking searches, which return ranked results with uncertainty. None of the above searchable encryption schemes is applicable to EMR data sets.
The electronic medical record has the characteristics of large data volume, multiple attributes and quick updating, and meanwhile, the electronic medical record belongs to personal sensitive information and is usually stored in an encrypted mode. Therefore, searching encrypted electronic medical records requires that accurate multi-keyword and scope searches be performed while returning results that are deterministic, which is a particular problem to be solved.
Disclosure of Invention
In order to carry out high-efficiency and dynamic searchable encryption on massive high-dimensional encrypted electronic medical record data, the invention provides a dynamic searchable encryption method for massive high-dimensional medical data. The method realizes multi-keyword accurate search based on the attribute range, realizes nonlinear search in the aspect of searching efficiency in the aspect of aiming at massive medical data, and can realize dynamic update of encrypted data in the aspect of searchable encryption retrieval.
A dynamic searchable encryption method for massive high-dimensional medical data is characterized by comprising the following steps:
s1, the medical center sets D = { D } to the electronic medical record data set 1 ,D 2 ,...,D N Encrypting to obtain ciphertext data, wherein N represents the total number of the electronic medical records, and D n Representing an nth electronic medical record; the medical center extracts all attribute values in the electronic medical record data set D to construct an attribute value set W = { W = { W } 1 ,W 2 ,...,W R R denotes attribute type total, W r A set of attribute values representing an r-th attribute type;
s2, the medical center constructs corresponding attribute hierarchical trees aiming at a plurality of attributes of the electronic medical records, and the electronic medical records D are obtained through the attribute hierarchical trees and the attribute value set n Generating an index vector I n ；
S3, the medical center processes the electronic medical record D by adopting the segmentation vector parameters n Index vector I of n Obtaining an electronic medical record D n A plurality of index subvectors;
s4, the medical center constructs an index tree according to the index subvectors of the electronic medical record data set D and distributes a sub-secret key for each layer of the index tree;
s5, the medical center encrypts each layer of the index tree according to the sub-secret key to obtain an encrypted index tree, and then uploads the ciphertext data and the encrypted index tree to the medical cloud server;
s6, the data user sends a search query request to the medical center, and the medical center processes the search query request to generate a search trapdoor and feeds the search trapdoor back to the data user;
s7, a data user sends a matching request to a medical cloud server and sends the searching trapdoor, the medical cloud server performs matching in an encrypted index tree according to the searching trapdoor, and a matched electronic medical record ciphertext is fed back to the data user;
s8, dynamically updating the data in the medical cloud server by the medical center; when the medical center adds the electronic medical record, the ciphertext data of the electronic medical record to be added, the corresponding encryption index, the corresponding search trapdoor and the ID information are uploaded to the medical cloud server, the medical cloud server searches on an encryption index tree according to the search trapdoor to find the largest attribute matching node, and the encryption search and the corresponding ID information of the electronic medical record to be added are added to the attribute matching node according to an index tree generation rule; when the medical center deletes the electronic medical record, the searching trapdoor corresponding to the attribute value of the electronic medical record needing to be deleted is uploaded to the medical cloud server, the medical cloud server conducts matching retrieval on the encrypted index tree according to the searching trapdoor, and the electronic medical record corresponding to the leaf node matched with the leaf node on the index tree and the ID of the leaf node is deleted.
Further, the process of constructing the attribute hierarchical tree includes:
s11, one electronic medical record represents an attribute record of one patient, wherein one attribute record comprises multiple attributes, and each attribute corresponds to one attribute value; dividing multiple attributes into two categories of numerical attributes and non-numerical attributes according to the attribute values;
s12, for the value attribute class: determining the maximum value range of the attribute value of the attribute, and taking the maximum value range as a root node; dividing the maximum value range according to the attribute value range and the attribute logic to obtain a plurality of sub-ranges, wherein each sub-range is used as a child node; dividing each sub-node in the same way to obtain a plurality of new sub-nodes, then continuing dividing until a single attribute value is divided, and taking the single attribute value as a leaf node to obtain an attribute hierarchical tree;
s13, for the non-numerical attribute class: determining the semantics of the attribute value of the attribute, defining a value range by using a semantic inclusion relationship, and selecting the maximum semantics as a root node; and carrying out layer-by-layer division according to the semantic inclusion relationship and the semantic parallel relationship to obtain the attribute hierarchical tree.
Further, constructing a vector representation for each path of the attribute hierarchical tree, including:
layering for attributesEach node of the tree is allocated with a vector, and the nodes comprise root nodes, child nodes and leaf nodes; when a certain node is positioned at the j-th level of the b-th level of the attribute hierarchical tree b Bits, then, are represented as
Wherein j b ∈J b ，J b Represents the total number of nodes at level B, B = {1, 2., B }, and B represents the total number of levels of the attribute hierarchical tree;
if node
There are K sub-nodes in the b-th layer, and the node
Is a node
The k-th child node of (1) is a node
Allocating K-dimensional vectors
And the K-dimensional vector
The k-th dimension value of (1) and the remaining dimension values of (0);
establishing a corresponding path for each attribute value
And representing the nodes in each path by using corresponding vectors, and combining all the vectors in each path to obtain the vector representation of each path. Further, the process of constructing the index tree is as follows:
s21, constructing a V + 1-layer index tree, taking an index vector of the electronic medical record, and equally dividing the index vector by adopting a segmentation vector parameter to obtain V index sub-vectors which are sequentially ordered;
s22, acquiring a first index sub-vector of the index vector, starting searching from Root of a Root node of the index tree, judging whether a value of a sub-node in a first layer of the index tree is the same as that of the first index sub-vector, if so, performing the step S23, otherwise, adding a sub-node in the first layer, wherein the value of the sub-node is the same as that of the first index sub-vector, and then entering the step S23;
s23, entering a V = {2, 3., V +1} layer of the index tree, judging whether a value of a child node of the V-th layer of the index tree is the same as that of a V-th index sub-vector, if so, performing a step S24, otherwise, adding a child node of the V-th layer, wherein the value of the child node is the same as that of the V-th index sub-vector, and then entering the step S24;
s24, judging whether the current index sub-vector is the last index sub-vector of the index vector, if so, finishing the addition of the index vector, and attaching the ID information of the electronic medical record corresponding to the index vector to the leaf node corresponding to the current index sub-vector, otherwise, returning to S23;
s24, repeating the steps S21-S24, and adding the index vectors of all the electronic medical records to obtain an index tree.
Furthermore, hierarchical traversal is performed from Root of the index tree, and the sub-key of each layer is represented as SK v ＝{S v ,M v,1 ,M v,2 }，S v Dividing an identification matrix for the nodes of the v-th layer, wherein the identification matrix is an L multiplied by 1 dimension 0-1 matrix, and L is the vector dimension of the child nodes of each layer of the index tree; m is a group of v,1 L × L dimension first invertible matrix, M, for the v-th layer v,2 And L × L dimension second invertible matrix of the v-th layer. The medical center according to the sub-secret key SK v Sequentially encrypting child nodes of the v-th layer of the index tree to generate a corresponding encrypted index, wherein the process comprises the following steps:
s31, enabling the ith position of the v-th layer of the index tree to be a child node b v,i By a random number epsilon v,i Obtain a new vector b v ′ ,i ；
S32, for new vector b v ′ ,i Performing P division to obtain
And
if S is v [i]=0, then
If S is v [i]=1, then
Wherein S v [i]A node partition identifier representing an ith child node of the v-th layer of the index tree;
s33, generating the ith sub-node b of the v layer of the index tree according to the result of S32 v,i Is expressed as
Further, the medical center extracts all attribute values in the electronic medical record data set D to construct an attribute dictionary; the process that the medical center generates the search trapdoor according to the search query request through the attribute dictionary comprises the following steps:
s41, obtaining attribute keywords in the search query request, mapping the attribute keywords to the positions of corresponding attribute values through an attribute dictionary, and obtaining a position vector q by combining an attribute hierarchical tree;
s42, negating the position vector Q to obtain a query vector Q, and processing the query vector Q by adopting a partition vector parameter to obtain V sequentially ordered query sub-vectors;
s43, the v-th query subvector d v Multiplication by a random number beta v Obtain the vector d v ′；
S44, paired vectors d v ' performing q partitioning to obtain
And
if S is v [i]If not =0, then
If S is v [i]=1, then
Wherein S v [i]A node division identifier for indicating the ith child node of the v-th layer of the index tree;
s45, generating the v-th query subvector d of the query vector Q according to the result of S44 v Corresponding search trapdoor, denoted as
The invention has the beneficial effects that:
the invention provides a dynamic searchable encryption method for massive high-dimensional medical data, which comprises a medical center, a medical cloud server and a data user, can quickly and accurately search encrypted EMR, supports multi-keyword connection with a complex structure, realizes attribute range query by utilizing an attribute hierarchical structure, provides a simple and convenient search mode for massive electronic medical record data, and improves the search efficiency.
The invention reduces the dimensionality of the index vector through the attribute hierarchical structure (namely the attribute hierarchical tree), reduces the calculated amount and the communication expense, constructs the encryption index tree at the same time, and realizes the nonlinear search efficiency.
There is no connectivity between queries in the method of the present invention, i.e. other subjects than the medical center cannot generate a new search trapdoor from the previous search trapdoor, specifically, even if two identical search query requests are added, because of the random number added, a difference will be generated when generating the search trapdoor, and the medical cloud server cannot deduce the relationship between the search trapdoors.
The invention also provides a dynamic updating method of data, which can flexibly process addition and deletion of EMR without locally storing the index tree and reduce the risk of leakage of the local index tree.
Drawings
FIG. 1 is an architectural diagram of an embodiment of the present invention;
FIG. 2 is a diagram of an age attribute hierarchical tree structure according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating an index vector according to an embodiment of the present invention;
FIG. 4 is a diagram of an index tree structure according to an embodiment of the present 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.
In the dynamic searchable encryption method for massive high-dimensional medical data provided by the invention, as shown in fig. 1, three entities, namely a medical center, a medical cloud server and a data user, are included.
A medical center is a hospital or medical facility that has EMR resources that function to: for electronic medical record data set D = { D 1 ,D 2 ,...,D N Encrypting to obtain ciphertext data, N represents the total number of electronic medical records, D n Representing an nth electronic medical record; extracting all attribute values of the electronic medical record dataset D to construct a set of attribute values W = { W = { W 1 ,W 2 ,...,W R R denotes attribute type total, W r A set of attribute values representing an r-th attribute type; uploading the encrypted electronic medical record data and the encrypted index tree to a medical cloud server; meanwhile, the system is responsible for examining the data users and sending search trapdoors and EMR decryption keys for the examined data users; and the medical center is also responsible for updating the data in the medical cloud server in real time, and when the data are updated, the medical center locally generates update information and then sends the update information to the medical cloud server.
The data user is an authorized unit which allows the encrypted data to be searched in the medical cloud server, when the data user inquires the encrypted data, firstly, a search inquiry request is sent to the medical center, the medical center is waited to feed back a corresponding search trapdoor and a decryption key, then the search trapdoor is sent to the medical cloud server, the medical cloud server is waited to feed back the encrypted data meeting the conditions, and then the encrypted data is decrypted by using the decryption key fed back by the medical center.
The medical cloud server is used for storing the encrypted electronic medical record data and the encrypted index tree and performing corresponding processing operation when receiving the search trapdoor and the updating request.
In one embodiment, as shown in table 1, an electronic medical record has four attributes, namely age, regional race, gender maker and disease, each attribute has multiple attribute values, but each attribute of an electronic medical record corresponds to a specific attribute value, and each electronic medical record has its own ID information.
TABLE 1 electronic medical records
As can be seen from table 1, the gender attribute has only two attribute values, male and female, which are convenient to represent, but the attribute values of the three attributes of age, region and disease are various and have complex relationships, and in order to implement the range search in the attribute domain and reduce the length of the index vector, the medical center constructs the attribute hierarchical tree for different types of attributes respectively.
In an embodiment, taking an age attribute as an example, as shown in fig. 2, a hierarchical tree of the age attribute is constructed, and first, a maximum value range capable of containing all age attribute values recorded in EMRs is determined, in this embodiment, the maximum value range is determined to be [0,100], the [0,100] is used as a root node of the hierarchical tree of the age attribute, the root node [0,100] is divided according to the attribute range and attribute characteristics, so as to obtain 3 child nodes of [0,30], [31,60] and [61,100], and then the three child nodes are divided respectively until only one specific attribute value remains, and the attribute value is used as a leaf node, such as "1", "2" at the bottom in fig. 2.
Specifically, for the attributes of non-numerical classes such as regions and diseases, the semantic containment relationship is used to define the range, so as to construct the corresponding attribute hierarchical tree. For example, semantically, the region "Sichuan" includes the place "Chengdu", and "Yunnan" and "Hunan" are parallel province names.
In one embodiment, the medical center generates an index vector for the electronic medical record through the attribute hierarchical tree and the attribute value set, and one index vector of one EMR is composed of location information of multiple attributes, which are described in this embodiment with four attributes of age, region, gender and disease.
Specifically, constructing a vector representation for each path of the hierarchical tree of attributes includes:
allocating vectors for each node of the attribute hierarchical tree, wherein the nodes comprise root nodes, child nodes and leaf nodes; when a certain node is positioned at the j-th level of the b-th level of the attribute hierarchical tree b Bit, then expressed as
Wherein j is b ∈J b ，J b Represents the total number of nodes at level B, B = {1,2, ·, B }, and B represents the total number of levels of the attribute hierarchical tree;
if the node
There are K sub-nodes in the b-th layer, and the node
Is a node
The k-th child node of (1) is a node
Allocating K-dimensional vectors
And the K-dimensional vector
The k-th dimension value of (1) and the remaining dimension values of (0);
establishing a corresponding path for each attribute value
And representing the nodes in each path by using corresponding vectors, and combining all the vectors in each path to obtain the vector representation of each path.
Specifically, the age attribute hierarchical tree is described, and as shown in fig. 2 and 3, the path corresponding to the attribute value "61" is P 61 ＝(a 1,1 ,a 2,3 ,a 3,7 ,a 4,61 ) = ("0-100", "61-100", "61-70", "61"), node a 2,3 = "61-100" is node a 1,1 Sub-node of the 3 rd bit of = "0-100", and node a 1,1 If there are 3 child nodes in total for "0-100", node a is obtained 2,3 = 61-100 for allocating a 3-dimensional vector a 2,3 =001; then the next node in the processing path, node a 3,7 = "61-70" is node a 2,3 Bit 1 of sub-node of = "61-100", and node a 2,3 If there are 4 child nodes in total for "61-100", node a is identified 3,7 = "61-70" allocate 4-dimensional vector a 3,7 =1000; node a 4,61 = "61" is node a 3,7 Bit 1 of sub-node of = "61-70", and node a 3,7 If the number of the child nodes is 10 in total, the node is the node a 4,61 =61 allocates a 10-dimensional vector a 4,61 =1000000000, resulting in a vector representation of attribute value "61" of 100110001000000000. Vector representations of four attributes of the age, the region, the gender and the disease of an EMR are combined to form an index vector of the EMR, wherein the gender has only two attribute values of a male and a female, an attribute hierarchical tree is not constructed, and the vector representation is carried out based on the positions of the attribute value sets.
In one embodiment, the medical center constructs an index tree from the index subvector of the electronic medical record data set D, including:
s21, constructing a V + 1-layer index tree, taking an index vector of the electronic medical record, and equally dividing the index vector by adopting a segmentation vector parameter to obtain V index sub-vectors which are sequentially ordered;
s22, acquiring a first index sub-vector of the index vector, starting searching from Root of a Root node of the index tree, judging whether a value of a sub-node in a first layer of the index tree is the same as that of the first index sub-vector, if so, performing the step S23, otherwise, adding a sub-node in the first layer, wherein the value of the sub-node is the same as that of the first index sub-vector, and then entering the step S23;
s23, entering a V = {2, 3., V +1} layer of the index tree, judging whether a child node value of the V layer of the index tree is the same as the value of a V index sub-vector, if so, performing a step S24, otherwise, adding a child node in the V layer, wherein the child node value is the same as the value of the V index sub-vector, and then entering the step S24;
s24, judging whether the current index sub-vector is the last index sub-vector of the index vector, if so, finishing the addition of the index vector, and attaching the ID information of the electronic medical record corresponding to the index vector to the leaf node corresponding to the current index sub-vector, otherwise, returning to S23;
and S25, repeating the steps S21-S24, and adding the index vectors of all the electronic medical records to obtain an index tree.
Specifically, as shown in fig. 4, there are 5 index vectors of [ 1000100010001 ], [100110001], [110001001], [110001111], [110010111], and the division vector parameter V is used to process the 5 index vectors respectively, in this embodiment, V =3, and 3 index sub-vectors [100,010,001], [100,110,001], [110,001,111], and [110,010,111] corresponding to the 5 index vectors are obtained.
Constructing a Root node Root of an index tree, firstly adding an index vector [10001 ], if the Root node Root has no child node of [100], adding a child node of [100], then searching a child node of [010] under the branch of the child node [100], if the child node of [010] is not added, then searching a child node of [001] under the branch of the child node [010], if the child node of [001] is not added, at this time, completely adding the index child vector of a first EMR, and adding ID information of the EMR at the last child node [001], namely a leaf node, wherein ID =1; after the first four index vectors are added, and finally, an index vector [110010111] is added, whether the Root node Root is a child node of [110] is judged, if the Root node Root is added before, whether the Root node Root is a child node of [010] is searched by a branch entering the child node [110], if the Root node Root is not added with [010], the child node [111] is created in [010], and finally, the ID information is attached, and the ID =5.
Specifically, hierarchical traversal is performed starting from Root of the index tree, and the sub-key of each layer is represented as SK v ＝{S v ,M v,1 ,M v,2 }，S v Dividing an identification matrix for the nodes of the v-th layer, wherein the identification matrix is an L multiplied by 1 dimension 0-1 matrix, and L is the vector dimension of the child nodes of each layer of the index tree; m v,1 Is a first invertible matrix of dimension L × L of the v-th layer, M v,2 Is an L × L dimension second reversible matrix of the v layer, and the medical center is based on the sub-secret key SK v Sequentially encrypting child nodes of the v-th layer of the index tree to generate a corresponding encrypted index, wherein the process comprises the following steps:
s31, enabling the ith position of the v-th layer of the index tree to be a child node b v,i By a random number epsilon v,i Get the new vector b v ′ ,i ；
S32, aiming at new vector b v ′ ,i Performing P division to obtain
And
if S is v [i]If not =0, then
If S is v [i]=1, then
Wherein S v [i]A node division identifier for indicating the ith child node of the v-th layer of the index tree;
s33, generating the ith bit child node b of the v layer of the index tree according to the result of S32 v,i Is expressed as
In one embodiment, the medical center extracts all attribute values in the electronic medical record data set D to construct an attribute dictionary; the process that the medical center generates the search trapdoor according to the search query request through the attribute dictionary comprises the following steps:
s41, obtaining attribute keywords in the search query request, mapping the attribute keywords to the positions of corresponding attribute values through an attribute dictionary, and obtaining a position vector q by combining an attribute hierarchical tree; for the attribute categories which are not searched by a data user, all attribute value position vector bits of the attribute categories are set to be 1, for the attribute categories which need to be searched, the corresponding attribute value position vector bits are set to be 1, and the rest attribute value position vector bits are set to be 0;
s42, negating the position vector Q to obtain a query vector Q, and processing the query vector Q by adopting a segmentation vector parameter to obtain V query sub-vectors which are sequentially ordered; the positions of all attribute values in the query vector Q correspond to the attribute value sets one by one, and attribute hierarchical trees are adopted to represent the attribute types with complex relations;
s43, the v-th query subvector d v Multiplication by a random number beta v Obtain the vector d v ′，β v ≠0；
S44, paired vectors d v ' performing q partitioning to obtain
And
if S is v [i]If not =0, then
If S is v [i]=1, then
Wherein S v [i]Representing the ith bit of the v-th layer of the index treeNode division identification of the nodes;
s45, generating the v-th query subvector d of the query vector Q according to the result of S44 v Corresponding search trapdoor, denoted as
In one embodiment, the medical cloud server executes a matching query after receiving a search trapdoor d ″ sent by a data user, and the matching query includes:
performing hierarchical traversal on the encryption index tree, starting from a root node, sequentially performing standard inner product on encryption indexes of child nodes on the v-th layer of the encryption index tree with the v-th search trapdoor of a query vector Q, if the result is 0, indicating that the child nodes are matched with the query attribute, then performing matching screening in branches of the child nodes, sequentially performing standard inner product on the encryption indexes of all child nodes of the child nodes with the v + 1-th search trapdoor of the query vector Q, searching to the last layer according to the rule, and pointing the nodes matched with the last layer of the encryption index tree to ID information of EMR (electronic magnetic resonance) encrypted data matched with the query of a data user, wherein the standard inner product is expressed as follows:
wherein epsilon is a random value added for generating an encryption index, and beta is a random value added for generating a search trapdoor.
In one embodiment, the medical center dynamically updates the data stored in the medical cloud server, and when the electronic medical record is deleted or added, the purpose is achieved by synchronously updating the nodes of the encryption index tree on the medical cloud server, and it is noted that the updating of the index is only based on the ID of the EMR encryption data and does not need to access the EMR data content.
Specifically, when the medical center adds EMRs, the EMRs to be added are firstly encrypted, the encryption indexes and the search trapdoors of the EMRs are generated by adopting the method mentioned in the foregoing, and then the encrypted EMRs, the IDs of the EMRs, the encryption indexes and the search trapdoors are sent to the medical cloud server.
After receiving a data adding request of a medical center, the medical cloud server searches and queries the encrypted index tree by using the search trapdoors of EMRs to be added based on the matching query mode mentioned in the above embodiment, after receiving the search trapdoors of the EMRs to be added, the medical cloud server starts to search from the root nodes of the encrypted index tree, performs standard inner product on the encrypted indexes of the child nodes at the v-th layer of the encrypted index tree sequentially with the v-th search trapdoor of the query vector Q, if the result is 0, the attribute values of the child nodes are the same as those of the EMRs to be added, performs matching screening in the branches of the child nodes, performs standard inner product on the encrypted indexes of all the child nodes of the child nodes sequentially with the v + 1-th search trapdoor of the query vector Q, and so on. In the search query process, if the v + i th layer of the encryption index tree is searched, only one node with the standard inner product of 0 exists, and the inner products of the child nodes of the node with the standard inner product of 0 and the v + i +1 th search trapdoor are not 0, the node is the attribute matching node of the EMR to be added, which is the largest in the encryption index tree. And starting from the v + i +1 th sub-vector of the encryption index of the EMR needing to be added, adding a sub-node for the maximum attribute matching node according to the construction rule of the index tree, continuing to add under the sub-node until the sub-vector used for adding the encryption index is added, namely a final leaf node is obtained, and adding the ID of the EMR needing to be added at the leaf node to finish the updating of the encryption index tree.
Specifically, the medical center needs to delete the abandoned medical record in the medical cloud server, so that waste of storage resources and reduction of search efficiency are avoided. In the case of a large amount of EMR data, an ID is not usually designated to delete a record, and more, deletion of a corresponding record is performed based on an attribute value that matches an attribute. When the medical center deletes the EMR, it first determines which attribute values, such as age =61and distance = diabetes, are deleted. And according to the condition of deleting the data, the medical center generates a corresponding search trapdoor and sends the search trapdoor to the medical cloud service. And the medical cloud server receives a data deletion request of the medical center, and searches the encrypted index tree for the search trapdoor for deleting the record in a search query mode. When the last layer is searched, there is a node that meets the requirements and that points to an ID that is the ID of the encrypted EMR that meets the delete data request. The medical cloud server needs to delete the node at the last level of the encrypted index tree and delete the encrypted EMR of the corresponding ID in the encrypted store.
In the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "connected," "fixed," "rotated," and the like are to be construed broadly, e.g., as being fixedly connected, detachably connected, or integrated; can be mechanically or electrically connected; the terms may be directly connected or indirectly connected through an intermediate agent, and may be used for communicating the inside of two elements or interacting relation of two elements, unless otherwise specifically defined, and the specific meaning of the terms in the present invention can be understood by those skilled in the art according to specific situations.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A dynamic searchable encryption method for massive high-dimensional medical data is characterized by comprising the following steps:
s1, the medical center sets an electronic medical record data set D = { D = { D = } to electronic medical record data 1 ,D 2 ,...,D N Encrypting to obtain ciphertext data, wherein N represents the total number of the electronic medical records, and D n Representing an nth electronic medical record; the medical center extracts all attribute values in the electronic medical record data set D to construct an attribute value set W = { W = { W } 1 ,W 2 ,...,W R R denotes attribute type total, W r A set of attribute values representing an r-th attribute type;
s2, the medical center constructs a corresponding attribute hierarchical tree for a plurality of attributes of the electronic medical recordElectronic medical record D with attribute hierarchical tree and attribute value set n Generating an index vector I n ；
S3, the medical center processes the electronic medical record D by adopting the segmentation vector parameters n Index vector I of n Obtaining an electronic medical record D n A plurality of index subvectors of (a);
s4, the medical center constructs an index tree according to the index subvectors of the electronic medical record data set D and distributes a sub-secret key for each layer of the index tree;
s5, the medical center encrypts each layer of the index tree according to the sub-secret key to obtain an encrypted index tree, and then uploads the ciphertext data and the encrypted index tree to a medical cloud server;
s6, the data user sends a search query request to the medical center, and the medical center processes the search query request to generate a search trapdoor and feeds the search trapdoor back to the data user;
s7, a data user initiates a matching request to a medical cloud server and sends the searching trapdoor, the medical cloud server performs matching in an encryption index tree according to the searching trapdoor, and a matched electronic medical record ciphertext is fed back to the data user;
s8, the medical center updates the data in the medical cloud server; when the medical center adds the electronic medical record, the ciphertext data of the electronic medical record to be added, the corresponding encryption index, the corresponding search trapdoor and the corresponding ID are uploaded to a medical cloud server; and when the medical center deletes the electronic medical record, uploading the search trapdoor corresponding to the attribute value of the electronic medical record to be deleted to the medical cloud server.
2. The dynamic searchable encryption method for massive high-dimensional medical data according to claim 1, wherein the process of constructing the attribute hierarchical tree comprises:
s11, one electronic medical record represents an attribute record of one patient, one attribute record comprises multiple attributes, and each attribute corresponds to one attribute value; dividing multiple attributes into two categories, namely a numerical attribute and a non-numerical attribute according to the attribute values;
s12, for the numerical value attribute class: determining the maximum value range of the attribute value of the attribute, and taking the maximum value range as a root node; obtaining a plurality of sub-ranges according to the attribute value range and the maximum value range of the logical division of the attribute, wherein each sub-range is used as a child node; dividing each sub-node in the same way to obtain a plurality of new sub-nodes, then continuing dividing until a single attribute value is divided, and taking the single attribute value as a leaf node to obtain an attribute hierarchical tree;
s13, for the non-numerical attribute class: determining the semantics of the attribute value of the attribute, defining a value range by using a semantic inclusion relationship, and selecting the maximum semantics as a root node; and carrying out layer-by-layer division according to the semantic inclusion relation and the semantic parallel relation to obtain the attribute hierarchical tree.
3. The method for dynamically searchable encryption of massive amounts of high-dimensional medical data according to claim 2, wherein constructing a vector representation for each path of a hierarchical tree of attributes comprises:
allocating vectors for each node of the attribute hierarchical tree, wherein the nodes comprise root nodes, child nodes and leaf nodes; when a certain node is positioned at the j-th level of the b-th level of the attribute hierarchical tree b Bit, then expressed as
Wherein j b ∈J b ，J b Represents the total number of nodes at level B, B = {1, 2., B }, and B represents the total number of levels of the attribute hierarchical tree;
if the node
There are K sub-nodes in the b-th layer, and the node
Is a node
The k-th child node of (1) is a node
Allocating K-dimensional vectors
And the K-dimensional vector
The k-th dimension value of (1) and the remaining dimension values of (0);
establishing a corresponding path for each attribute value
And representing the nodes in each path by using corresponding vectors, and combining all the vectors in each path to obtain the vector representation of each path.
4. The dynamic searchable encryption method for massive high-dimensional medical data according to claim 1, wherein the process of constructing the index tree is as follows:
s21, constructing a V + 1-layer index tree, taking an index vector of the electronic medical record, and equally dividing the index vector by adopting a segmentation vector parameter to obtain V index sub-vectors which are sequentially ordered;
s22, obtaining a first index sub-vector of the index vector, starting searching from Root of a Root node of the index tree, judging whether a first layer of the index tree has a sub-node with the same value as the first index sub-vector, if so, performing a step S23, otherwise, adding a sub-node in the first layer, wherein the value of the sub-node is the same as the value of the first index sub-vector, and then entering the step S23;
s23, entering a V = {2, 3., V +1} layer of the index tree, judging whether a value of a child node of the V-th layer of the index tree is the same as that of a V-th index sub-vector, if so, performing a step S24, otherwise, adding a child node of the V-th layer, wherein the value of the child node is the same as that of the V-th index sub-vector, and then entering the step S24;
s24, judging whether the current index sub-vector is the last index sub-vector of the index vector, if so, finishing the addition of the index vector, and attaching the ID information of the electronic medical record corresponding to the index vector to the leaf node corresponding to the current index sub-vector, otherwise, returning to S23;
and S25, repeating the steps S21-S24, and adding the index vectors of all the electronic medical records to obtain an index tree.
5. The dynamic searchable encryption method for massive high-dimensional medical data according to claim 4, wherein hierarchical traversal is performed starting from Root of an index tree, and a sub-key of each layer is represented as SK v ＝{S v ,M v,1 ,M v,2 }，S v Dividing an identification matrix for the nodes of the v-th layer; m is a group of v,1 Is the first invertible matrix of the v-th layer, M v,2 As a second reversible matrix at layer v, the medical center is based on the sub-key SK v Sequentially encrypting child nodes on the v-th layer of the index tree to generate a corresponding encryption index, wherein the process comprises the following steps:
s31, enabling the ith position of the v-th layer of the index tree to be a child node b v,i By a random number epsilon v,i Obtain a new vector b v ′ ,i ；
S32, aiming at new vector b v ′ ,i Performing P partition to obtain
And
if S is v [i]If not =0, then
If S is v [i]=1, then
Wherein S v [i]A node division identifier for indicating the ith child node of the v-th layer of the index tree;
s33. According to S32 the ith bit child node b of the v-th level of the resulting index tree v,i Is expressed as
6. The dynamic searchable encryption method for massive high-dimensional medical data according to claim 1, wherein the medical center extracts all attribute values in an electronic medical record data set D to construct an attribute dictionary; the process that the medical center generates the search trapdoor according to the search query request through the attribute dictionary comprises the following steps:
s41, obtaining attribute keywords in the search query request, mapping the attribute keywords to the positions of corresponding attribute values through an attribute dictionary, and obtaining a position vector q by combining an attribute hierarchical tree;
s42, negating the position vector Q to obtain a query vector Q, and processing the query vector Q by adopting a segmentation vector parameter to obtain V query sub-vectors which are sequentially ordered;
s43, the v-th query subvector d v Multiplication by a random number beta v Obtain the vector d v ′；
S44, paired vectors d v ' performing q partitioning to obtain
And
if S is v [i]=0, then
If S is v [i]=1, then
Wherein S v [i]A node division identifier for indicating the ith child node of the v-th layer of the index tree;
s45, generating the v-th query sub direction of the query vector Q according to the result of S44Quantity d v Corresponding search trapdoor, denoted as
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117271711A (en) * | 2023-11-21 | 2023-12-22 | 湖南格尔智慧科技有限公司 | Medical case retrieval method and system based on similarity calculation |
| CN117521118A (en) * | 2024-01-05 | 2024-02-06 | 深圳万海思数字医疗有限公司 | Medical data searchable encryption privacy protection and system |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117271711A (en) * | 2023-11-21 | 2023-12-22 | 湖南格尔智慧科技有限公司 | Medical case retrieval method and system based on similarity calculation |
| CN117521118A (en) * | 2024-01-05 | 2024-02-06 | 深圳万海思数字医疗有限公司 | Medical data searchable encryption privacy protection and system |
| CN117521118B (en) * | 2024-01-05 | 2024-04-26 | 深圳万海思数字医疗有限公司 | Medical data searchable encryption privacy protection and system |
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