Dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on block chain

Abstract

The invention discloses a dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on a block chain, and relates to the technical field of cloud ciphertext retrieval. The method is based on four entities, namely a data manager, a cloud server, a block chain and a data user, and specifically comprises the following contents: the data manager is responsible for encrypting a document set and constructing an encryption index, and respectively storing the document set and the encryption index in the cloud server and the block chain; and for the newly added document, the data manager sends an updating instruction, and the cloud server and the block chain update the encryption set and the index in real time. And the data user submits a query request to the block chain by calculating and searching the trap door. And the block chain executes matching operation and result verification, and finally returns an accurate and complete search result to the data user.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud-based ciphertext retrieval, and in particular to a blockchain-based dynamic verifiable fuzzy multi-keyword cloud-based ciphertext search method. Background Art

[0002] In the rapid development of the information age, cloud computing, as an innovative technological revolution, provides customers with an economical and convenient data processing platform with its flexible and efficient resource allocation. As a core component of cloud computing, it greatly simplifies the data storage and access process, promotes collaborative work on a global scale, and reduces the operation and maintenance costs of enterprises. However, in this booming context, the balance between user data privacy protection and search performance in cloud computing environments is particularly critical. Although searchable encryption technology has played a vital role in cloud computing scenarios, it allows users to search on encrypted data. This technology combines cryptography and search technology, allowing data owners to effectively retrieve encrypted data while protecting data privacy and protecting the security of data during transmission and storage, but it still faces many challenges.

[0003] In the search strategy for encrypted data, traditional considerations mainly focus on two major challenges in meeting user needs: On the one hand, due to the potential inaccuracy of user query keyword input (such as spelling errors, synonyms, etc.), the traditional exact match search mechanism often cannot provide a wider range of search matches, resulting in limited accuracy and practicality of search results. On the other hand, the dynamic growth of data caused by the progress and diversified needs of the information age is obvious, and institutions need to respond immediately and seamlessly add dynamically generated data to the cloud to ensure the real-time availability and integrity of the data.

[0004] In addition, trust issues in cloud environments are becoming increasingly prominent. Security risks in cloud servers may cause them to intentionally or unintentionally return incorrect search results, posing a threat to data accuracy and user trust. This lack of trust not only affects users' reliance on cloud services, but also limits the further development of cloud computing technology.

[0005] Therefore, in view of the requirements of user query fault tolerance and dynamic data addition, it is particularly important to study the fuzzy multi-keyword search mechanism that supports dynamic updates. This mechanism can improve the adaptability of the search system to the uncertainty of word input, including accepting spelling errors, synonyms and related words, and providing more flexible query matching to cope with the diversity of user input. At the same time, the mechanism must also adapt to the dynamic environment of real-time data growth, ensuring real-time response to encrypted data updates and the effectiveness of index management. For the problem of false deception by malicious cloud servers, the research on the result verification mechanism based on blockchain will effectively prevent data manipulation behaviors such as illegal tampering and search omissions, ensure the correctness and integrity of matching results, and thus enhance users' trust in search results. This mechanism can not only improve the reliability of search results, but also provide users with transparent operation records, further enhancing users' trust in cloud services.

[0006] At present, the implementation of a dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain faces several key challenges, the most prominent of which are the design of keyword fuzzification, encryption index construction and result verification mechanism. Specifically, how to efficiently build an encrypted index that can both protect data privacy and adapt to dynamic changes after the fuzzification of keywords, while ensuring that the comprehensive verification requirements for the correctness and integrity of the results are met after the search operation is completed, is the core problem that needs to be solved urgently. In order to solve the above problems, the present invention proposes a dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain. Summary of the invention

[0007] The purpose of the present invention is to propose a dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain to solve the fault tolerance requirements faced by user queries in the cloud computing environment proposed in the background technology, the challenge of dynamic data growth, and the problem of false results that may be returned by potential malicious servers. The present invention can provide a safer and more efficient solution for ciphertext retrieval in the cloud computing environment, and promote the further development and application of cloud computing technology.

[0008] To achieve the above object, the present invention provides the following solutions:

[0009] The dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain includes the following contents:

[0010] S1. Perform vectorization conversion on the characteristic keywords and implement fuzzy processing;

[0011] S2, calculate the inserted value and summary information, and build an encrypted index;

[0012] S3, Synchronize the newly added ciphertext data and implement the extended encryption index;

[0013] S4, calculating and generating a corresponding search trap according to the query conditions input by the user;

[0014] S5, traverse the encrypted index from top to bottom and perform matching search;

[0015] S6. Perform comprehensive verification of the correctness and completeness of the search results.

[0016] Preferably, the S1 specifically includes the following contents:

[0017] S1.1. The data manager obtains from each data file f i ∈F to extract feature keywords and construct the index keyword set W(f i ), and then use the symmetric encryption algorithm AES to encrypt the corresponding data document c i ;

[0018] S1.2. The data manager uses a uni-gram-based vectorization algorithm to convert each index keyword into a 160-dimensional unigram vector.

[0019] S1.3, data managers use the locality-sensitive hash function family The unary graph vector of the index keyword is used as input to calculate the bucket string value S after fuzzy processing.

[0020] Preferably, S2 specifically includes the following contents:

[0021] S2.1. The data manager creates a hash table based on the bucket string value S after vectorization and obfuscation, which serves as the actual storage structure of the encrypted index which is logically a binary tree.

[0022] S2.2, the data manager converts the document identifier i into a binary string encoding value path (leaf i ), which is the leaf node of the logical tree i The path encoding value of nodes(leaf i ) is the root node to the leaf node leaf i The set of all traversed nodes on this path, where the path encoding of each node v Is the leaf node encoding value path(leaf i ), the encoding rule is to add character 0 to the path value of the left child node, add character 1 to the path value of the right child node, and the path value of the root node is a null character;

[0023] S2.3, the data manager uses the pseudo-random function F k With the random oracle H1, calculate the bucket string value S and the path encoding value, obtain the insertion value of each node and index keyword, and store it in the encrypted index J;

[0024] S2.4. The data manager shall follow the encrypted document c i The leaf node corresponding to the document identifier i Path encoding path(leaf i ), calculate the summary information d i , and store it in the encrypted index J;

[0025] S2.5. The data manager outsources the encrypted document set C and encrypted index J to the cloud server and blockchain for storage and management respectively.

[0026] Preferably, S3 specifically includes the following contents:

[0027] S3.1. The data manager extracts the characteristic keywords of the newly added documents to build an index set and encrypts the documents using the AES algorithm;

[0028] S3.2. Data managers use uni-gram vectorization algorithms and hash function families Calculate the bucket string value set, then convert the document identifier into a binary string to obtain the traversal node set and path value set;

[0029] S3.3. The data manager calculates the insertion value set and summary information corresponding to the document based on the traversed node set and bucket string value set, and sends it and the ciphertext as an update request to the blockchain and the server;

[0030] S3.4. The server stores the newly sent ciphertext data. The blockchain receives the update request and stores the inserted value set and summary into the encrypted index J to obtain the updated index J. up .

[0031] Preferably, the S4 specifically includes the following contents:

[0032] S4.1. Data users use the uni-gram algorithm and hash function family based on the input query Q = {q1, q2, ...} Process each q i ∈Q, get the bucket string value of the query;

[0033] S4.2. Data users use the pseudo-random function F k And the random oracle H1, calculate the root node empty character and each bucket string value, get the search trapdoor TK = {tk1, tk2, ...}, then send the trapdoor TK as a query request to the blockchain.

[0034] Preferably, the S5 specifically includes the following contents:

[0035] S5.1. The blockchain receives the search trap TK={tk1, tk2, ...} sent by the user, and performs a top-down search traversal on the encrypted index J of the binary tree at the logical level;

[0036] S5.2, blockchain performs node verification. According to the search trap TK = {tk1, tk2, ...}, the current traversal node v (the initial traversal node starts from the root node), the blockchain uses the pseudo-random function F k and random oracle H1 to calculate the target insertion value Determine whether the encrypted index J contains TK; if J contains all TK, the current node matches the search request of the data user, and the child nodes are recursively checked until the leaf node is reached, otherwise the search stops, and finally the matching leaf node path set R is obtained as the result set;

[0037] S5.3. Blockchain records auxiliary information. Regardless of whether the current node meets the matching conditions, the blockchain records its path encoding value and matching result (0 for no match and 1 for match) as well as the summary of the leaf node to the auxiliary proof set AP.

[0038] Preferably, the S6 specifically includes the following contents:

[0039] S6.1. The blockchain sends a request to the cloud server based on the search result set R, and the cloud server returns the corresponding ciphertext result E;

[0040] S6.2. The blockchain recalculates the summary information based on the ciphertext result set E and the result set R. Judgement i ′ and the summary set D in the auxiliary information set AP are equal, if d i ′=d i , then the result correctness verification is passed, and the ciphertext result is guaranteed not to be illegally tampered with;

[0041] S6.3. The blockchain constructs a search binary tree based on the encoding values ​​and matching results of the traversed nodes in the auxiliary information set AP. The blockchain verifies whether the search result of its non-leaf terminal node is 0. If the result is 0, it means that the match in the child node is denied, proving that downward search is unnecessary, and the integrity verification of the search result is passed. Otherwise, a non-zero result indicates that the traversal retrieval is incomplete and some nodes are missed in the search.

[0042] Compared with the prior art, the present invention provides a dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain, which has the following beneficial effects:

[0043] The present invention innovatively integrates blockchain technology with fuzzy algorithms and dynamic index structures, and proposes a dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain. Specifically, the present invention combines fuzzy processing algorithms with highly flexible indexing mechanisms, which not only ensures that the needs of dynamic data updates can be responded to, but also effectively realizes the robust processing of spelling errors that are prevalent in user queries, and improves the flexibility and fault tolerance of query operations. The present invention utilizes the distributed ledger characteristics and tamper-proof nature of blockchain technology, designs a verification method, and realizes the comprehensive verification of the correctness and integrity of ciphertext results by recalculating summary information and constructing a search binary tree. Therefore, on the basis of effectively meeting the needs of data updates, the present invention successfully realizes the support for fuzzy multi-keyword search functions and significantly enhances the reliability of ciphertext search results. This invention not only provides a more efficient, flexible and secure solution for information retrieval in a cloud computing environment, but also opens up a new path for the application of blockchain technology in the field of data retrieval. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings involved in the embodiments are briefly introduced. Obviously, the drawings in the following description are only schematic illustrations of some embodiments of the present invention. For those skilled in the art, other forms of drawings can also be constructed based on these drawings without creative work.

[0045] Figure 1 This is a model diagram of a blockchain-based dynamic verifiable fuzzy multi-keyword cloud ciphertext search method proposed by the present invention;

[0046] Figure 2 This is a specific example of constructing an encrypted index proposed in Embodiment 1 of the present invention;

[0047] Figure 3 This is an example proposed in Embodiment 1 of the present invention of constructing a search binary tree based on the auxiliary proof information that the search result is c1 to verify the integrity of the search result. DETAILED DESCRIPTION

[0048] The following is a clear and comprehensive description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. It should be noted that the embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more clear, the technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0050] Embodiment 1:

[0051] This example proposes a dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain, which includes the following steps:

[0052] Step 1: Vectorize the feature keywords and implement fuzzy processing

[0053] The data manager is responsible for outsourcing the document set F = {f1,f2,…,f n}, for each document f i ∈F, the data manager extracts the feature keyword set W(f i )={w1,w2,w3,…}, and use AES algorithm to encrypt the document to obtain the encrypted document set C={c1,c2,…,c n}.

[0054] The data manager uses a uni-gram-based vectorization algorithm to convert the keyword w j ∈W(f i ) to a vector Then use the locality sensitive hash function family Calculate the bucket string value

[0055] Step 2: Calculate the inserted value and summary information and build an encrypted index

[0056] For document f i ∈F, the corresponding leaf node is the document leaf i , the traversal node set from the root node to the leaf node is represented as nodes(leaf i ), the path encoding of each node is The data manager uses a pseudorandom function F k and random language H1, calculate the bucket string value and path encoding Get index insertion value According to the encrypted document c i ∈C and the leaf node encoding value, calculate the summary information d i =F k (path(leaf i )||c i ).

[0057] The data manager creates a hash table to save all inserted values ​​and summary information, obtains the encrypted index J and uploads it to the blockchain, and outsources the ciphertext set to the cloud server for storage and management.

[0058] Figure 3A specific example of building an encrypted index is shown. During the insertion of file f1 containing keyword "c", the file identifier "1" is converted into the path code "01" and the encrypted entries are sequentially inserted from the root to the leaf node located at the path "01". Specifically, the insertion value is calculated as Get the collection:

[0059] {H1(""||F k (S c )),H1("0"||F k (S c )),H1("01"||F k (S c ))}.

[0060] Next, the summary information d1 is calculated as F k (path(leaf1)||c1), where c1 represents the ciphertext of f1, and path(leaf1) represents the path "01" to the leaf node of f1.

[0061] Step 3: Synchronously add ciphertext data and expand the encryption index in real time

[0062] For each newly added document f i ′, the feature keyword is W(f i ′)={w1′,w′2,Λ},leaf i ′ is the leaf node of the corresponding logical index tree. The ciphertext encrypted using the AES algorithm is c i ′. For each keyword w i ′∈W(f i ′), the data manager converts w i ′Convert to vector Using hash function families Calculating vectors Corresponding bucket string value Then, the data manager calculates the value of the pseudo-random function F k and random language H1, calculate the inserted value Calculate summary information d i ′=F k (path(leaf i ′)||c i ′). The data manager sends the ciphertext, inserted value and summary information as an update request for dynamically added data to the cloud server and blockchain.

[0063] Step 4: Calculate and generate the corresponding search trap based on the query conditions entered by the user

[0064] The data user enters a set of query conditions Q = {q1,q2,…,q m}, the set of conditions contains m query keywords. The data user has a query keyword q i ∈Q, converted into a query vector using the uni-gram vectorization algorithm The corresponding bucket string value is calculated using the hash function family

[0065] The data user then uses the pseudo-random function F k For each bucket string value Encrypt and generate a token

[0066] After processing all query keywords, the data user constructs a search trap TK = {tk1, tk2, …, tk m}, sent to the blockchain to request retrieval, where m represents the total number of query keywords in Q.

[0067] Step 5: Traverse the encrypted index from top to bottom and perform matching searches

[0068] Data users send search requests TK = {tk1, tk2, ..., tk m}, the smart contract of the blockchain uses a top-down traversal method to retrieve the stored encrypted index J. The current search node v, whose node path encoding value is path(v), the smart contract is for each sub-trapdoor tk i ∈TK, use the random oracle function H1 to calculate the target insertion value If the encrypted index J contains all the target insertion values, the current node v satisfies the query condition of the data user. If the node v is a leaf node, the smart contract stops searching and returns the leaf node path encoding path(v), which is recorded as the result set R, which corresponds to a convertible decimal document identifier. If v is not a leaf node, recursively search its left and right child nodes, which is achieved by adding the character "0" or "1" to the path encoding path(v).

[0069] In addition to checking the matching status of nodes for query conditions, the blockchain also records auxiliary information AP during the traversal process. Specifically, regardless of whether node v matches, its path encoding value path(v) and matching status 0 or 1 (0 means the node does not match the query condition, 1 means the node matches the query condition) are saved in AP, and the summary information d of the leaf node is i Recorded as summary set D.

[0070] Step 6: Perform comprehensive verification of the correctness and completeness of the search results

[0071] The blockchain is based on the search result set R = {r1, r2, ..., rk}, sends a request to the cloud server, and the cloud server returns the corresponding ciphertext result set E = {e1, e2, ..., e k}.

[0072] The blockchain executes the verification algorithm to verify correctness. Summary set D = {d1, d2, ..., d k}, the blockchain verification contract uses a pseudo-random function F k and random oracle function H1, recalculate the summary value d to be compared i ′=F k (r i ||e i ), if any digest value d i ′=d i , the correctness verification passes and the stored encrypted document has not been tampered with.

[0073] Verify the integrity of the contract. According to the path encoding value path recorded in the auxiliary information AP j The verification contract builds a search binary tree based on the matching results. The contract checks whether the matching result of its non-leaf terminal node is 0. If so, the child node is denied to match, and there is no need to continue searching downwards. The verification passes. Otherwise, the verification fails, the traversal process of the search contract is incomplete, and there are missing nodes.

[0074] The correctness and integrity of the verification contract are verified to have passed. The blockchain sends the verification result of true to the data user, and the data user receives the ciphertext result E={e1,e2,…,e k}.

[0075] Figure 3 Let's show a specific verification scenario. A search query Q = {w1, w2}, in a set of 8 documents, only document f1 contains these two keywords. Therefore, it is represented as path(N 32 ) = "001" node N 32 The binary path is merged into the search result R, and the associated ciphertext digest d1 is archived in D. The auxiliary proof set AP = {"","0","1","00","01","000","001"} encapsulates the path encoding of all traversed nodes, and the matching result of each node is formalized as {1,1,0,1,0,0,1}, where 1 and 0 respectively indicate whether the corresponding node matches the search trapdoor or not.

[0076] In the verification phase, a search binary tree is constructed based on these search paths and node matching results to determine the completeness of the search path and ensure that no nodes are missed. Subsequently, d1′=F is recalculated according to the equation k (path(N 32)||c1)=F k ("001"||c1) is compared with the original d1, d1′=d1, and the correctness verification is passed.

[0077] The remaining technical features in this embodiment can be flexibly selected by those skilled in the art according to actual conditions to meet different specific practical needs. However, it is obvious to those of ordinary skill in the art that it is not necessary to adopt these specific details to implement the present invention. In other examples, in order to avoid confusing the present invention, the composition, structure or components of the formula are not specifically described, and they are all within the technical protection scope defined by the technical solution claimed for protection in the claims of the present invention.

[0078] Modifications and changes made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the scope of protection of the claims attached to the present invention. In the above description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details are not necessary to practice the present invention. In other examples, in order to avoid confusing the present invention, well-known technologies, such as specific construction details, operating conditions and other technical conditions, are not specifically described.

[0079] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain, characterized in that: Includes the following: S1. Perform vectorization conversion on the characteristic keywords and implement fuzzy processing; S2, calculate the inserted value and summary information, and build an encrypted index; S3, Synchronize the newly added ciphertext data and implement the extended encryption index; S4, calculating and generating a corresponding search trap according to the query conditions input by the user; S5, traverse the encrypted index from top to bottom and perform matching search; S6. Perform comprehensive verification of the correctness and completeness of the search results.

2. According to claim 1, the dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain is characterized in that: The S1 specifically includes the following contents: S1.

1. The data manager obtains from each data file f i ∈F to extract feature keywords and construct the index keyword set W(f i ), and then use the symmetric encryption algorithm AES to encrypt the corresponding data document c i ; S1.

2. The data manager uses a uni-gram-based vectorization algorithm to convert each index keyword into a 160-dimensional unigram vector. S1.3, data managers use the locality-sensitive hash function family The unary graph vector of the index keyword is used as input to calculate the bucket string value S after fuzzy processing.

3. The dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain according to claim 2 is characterized in that: The S2 specifically includes the following contents: S2.

1. The data manager creates a hash table based on the bucket string value S after vectorization and obfuscation, which serves as the actual storage structure of the encrypted index which is logically a binary tree. S2.2, the data manager converts the document identifier i into a binary string encoding value path (leaf i ), which is the leaf node of the logical tree i The path encoding value of nodes(leaf i ) is the root node to the leaf node leaf i The set of all traversed nodes on this path, where the path encoding of each node v It is a substring of the leaf node encoding value path(leafi). The encoding rule is to add the character 0 to the path value of the left child node, add the character 1 to the path value of the right child node, and the path value of the root node is an empty character. S2.3, the data manager uses the pseudo-random function F k With the random oracle H1, calculate the bucket string value S and the path encoding value, obtain the insertion value of each node and index keyword, and store it in the encrypted index J; S2.

4. The data manager shall follow the encrypted document c i The leaf node corresponding to the document identifier i Path encoding path(leaf i ), calculate the summary information d i , and store it in the encrypted index J; S2.

5. The data manager outsources the encrypted document set C and encrypted index J to the cloud server and blockchain for storage and management respectively.

4. The dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain according to claim 3 is characterized in that: The S3 specifically includes the following contents: S3.

1. The data manager extracts the characteristic keywords of the newly added documents to build an index set and encrypts the documents using the AES algorithm; S3.

2. Data managers use uni-gram vectorization algorithms and hash function families Calculate the bucket string value set, then convert the document identifier into a binary string to obtain the traversal node set and path value set; S3.

3. The data manager calculates the insertion value set and summary information corresponding to the document based on the traversed node set and bucket string value set, and sends it and the ciphertext as an update request to the blockchain and the server; S3.

4. The server stores the newly sent ciphertext data. The blockchain receives the update request and stores the inserted value set and summary into the encrypted index J to obtain the updated index J. up .

5. The dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain according to claim 4 is characterized in that: The S4 specifically includes the following contents: S4.

1. Data users use the uni-gram algorithm and hash function family based on the input query Q = {q1, q2, ...} Process each q i ∈Q, get the bucket string value of the query; S4.

2. Data users use the pseudo-random function F k And the random oracle H1, calculate the root node empty character and each bucket string value, get the search trapdoor TK = {tk1, tk2, ...}, then send the trapdoor TK as a query request to the blockchain.

6. The dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on blockchain according to claim 5 is characterized in that: The S5 specifically includes the following contents: S5.

1. The blockchain receives the search trap TK={tk1, tk2, ...} sent by the user, and performs a top-down search traversal on the encrypted index J of the binary tree at the logical level; S5.2, blockchain performs node verification. According to the search trap door TK = {tk1, tk2, ...}, the current traversal node v, the blockchain uses the pseudo-random function F k and random oracle H1 to calculate the target insertion value H1(path(v)||tk i ) tki∈TK =H1(path(v)||F k (S i )), determine whether the encrypted index J contains TK; if J contains all TK, the current node matches the search request of the data user, and continues to recursively check the child nodes until the leaf node, otherwise the search stops, and finally obtains the matching leaf node path set R as the result set; S5.

3. Blockchain records auxiliary information. Regardless of whether the current node meets the matching conditions, the blockchain records its path encoding value and matching result as well as the summary of the leaf node to the auxiliary proof set AP.

7. The large aperture zoom objective lens for an infrared polarization imaging system according to claim 6, characterized in that: The S6 specifically includes the following contents: S6.

1. The blockchain sends a request to the cloud server based on the search result set R, and the cloud server returns the corresponding ciphertext result E; S6.

2. The blockchain recalculates the summary information based on the ciphertext result set E and the result set R. Determine d′ i Is the summary set D in the auxiliary information set AP equal to d′? i =d i , then the result correctness verification is passed, and the ciphertext result is guaranteed not to be illegally tampered with; S6.3, the blockchain constructs a search binary tree based on the encoding values ​​and matching results of the traversed nodes in the auxiliary information set AP; the blockchain verifies whether the search result of its non-leaf terminal node is 0; If the result is 0, it means that the match in the child node is denied, proving that downward search is unnecessary and the integrity verification of the search results is passed; otherwise, a non-zero result indicates that the traversal retrieval is incomplete and some nodes are missed in the search.

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