CN112966086A - Verifiable fuzzy search method based on position sensitive hash function - Google Patents
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Abstract
The invention relates to a verifiable fuzzy search method based on a position sensitive hash function. The invention mainly comprises the following steps: on the basis, a verifiable fuzzy query index structure is constructed by combining a Mercker tree model, and node fingerprints and verification objects thereof are generated by using an HMAC function, so that verifiable multi-keyword fuzzy query aiming at English documents is realized. The method provides a solution for multi-keyword fuzzy search of encrypted outsourced data, tolerates possible keyword spelling errors by using a position sensitive hash function, improves the accuracy of fuzzy query matching, meanwhile supports the verification of the correctness and integrity of a returned file set, realizes the capture and prevention of malicious cloud server fraud behaviors, and has practical application value in common scenes of query keyword spelling errors and cloud server fraud in the field of cloud computing.
Description
Technical Field
The invention relates to the field of cloud computing and searchable encryption, in particular to a verifiable fuzzy search method based on a position sensitive hash function.
Background
The customizable storage service provided by the cloud server greatly saves the storage cost of enterprises and users. Anytime and anywhere network access and high quality storage management have led to an increasing choice of businesses and individuals uploading data to the cloud. However, cloud servers are not fully trusted and outsourcing sensitive information (e.g., email, credit card billing, health records, business confidential data, etc.) to a remote server can present privacy concerns. In order to protect the privacy of sensitive data, data owners often upload data to the cloud after encrypting the data locally, so searchable encryption technology has been widely studied. However, the conventional searchable encryption technology cannot meet the requirements of real search scenes because the cloud server cannot accurately return files satisfying the user query when the input keywords are misspelled or the tenses are inconsistent. An effective way to support fuzzy search is to cover all possible keyword spelling errors by expanding the index. For example, a fuzzy query method based on edit distance is proposed in the document "fuzzy keyword search over encrypted data in closed computing. Although the introduction of wildcards reduces the size of the predefined keyword set, the extended index still results in lower retrieval efficiency and additional index space overhead, which is not advantageous for pay-per-demand cloud storage services.
Position sensitive Hashing (Locality Hashing) is firstly applied to fast approximate search of high-dimensional mass data, two input items with higher similarity are mapped into the same hash value with high probability through hash collision to realize approximate retrieval, and documents of 'Privacy-preserving multi-keyword fuzzy search over encrypted data in the closed. INFOCOM, 2014' construct a file index vector by utilizing P stable position sensitive Hashing, and obtain a correlation score between a file and a query through matrix calculation. The scheme does not need a predefined fuzzy keyword set, but solves the problem of multi-keyword fuzzy query through algorithm design, and provides a new idea for solving the problem of fuzzy search.
The merkel tree (Merkle hash tree) is a special tree structure that can authenticate a group of messages with low computational overhead. The tree node stores a fingerprint which uniquely identifies the node, and the unidirectionality and low collision of the HMAC function ensure that any malicious cloud server cannot forge any result by generating the same root digest.
In recent years, there has been a certain research on the efficiency problem of fuzzy query, and a fuzzy query scheme based on a balanced binary tree structure appears, and the document "Efficient dynamic multikeyword search over encrypted closed data," Journal of Network and computer applications,2020 "utilizes a balanced binary tree and pruning algorithm to improve the retrieval efficiency and can complete multi-keyword fuzzy query in a sub-linear time.
Even if the cloud service provider is honest, the cloud service provider may cause the outsourced data files to be maliciously tampered and deleted when the cloud server is attacked by a virus or worm, in view of the fact that the cloud service provider may delete unusual outsourced data or forge search results to deceive users for the purpose of saving computing and storage costs. Therefore, a search scheme requiring a multi-keyword fuzzy query should be able to give the user the ability to verify the correctness and completeness of the returned search results.
Based on the above, the patent aims to provide an efficient and verifiable multi-keyword fuzzy query method, and the method can still accurately return a file set interested by a user even under the condition that the keywords are tense or misspelled.
Disclosure of Invention
The invention provides a verifiable fuzzy search method based on a position-sensitive hash function, which can efficiently and accurately return search results interested by a user even if a temporal state or misspelling occurs to a keyword in a user query request, and can check the correctness and integrity of the returned search results to prevent malicious cloud server fraudulent behaviors. The method mainly comprises the following steps:
(1) providing a fuzzy query search framework based on a position sensitive hash function;
(2) a verifiable fuzzy query index structure based on the Mercker tree is provided.
The specific contents are as follows:
(1) a fuzzy query search framework based on a position sensitive hash function is provided, and the model structure is shown in FIG. 1. The model consists of three entities, namely a data owner, a data user and a cloud server. The data owner firstly extracts a keyword set W from a data set F, establishes an encrypted index tree I according to the keyword set, finally uploads the encrypted data set C and the encrypted index tree I to the cloud, and distributes a decryption key and key information generated by a trapdoor TD to authorized data users through a secure channel; an authorized data user generates a trapdoor TD containing t query keywords and sends the trapdoor TD to a cloud server for query operation; after receiving a query request from a data user, the cloud server executes a predefined retrieval algorithm, and returns k documents with the highest relevance and a verification object set VO as a search result.
In the keyword processing stage, firstly, a keyword set W ═ W is extracted from the file set F1，w2，...，wkAnd calculating the TF-IDF value of each keyword in each file according to a TF-IDF (word frequency-inverse text frequency) rule as a weight, wherein the TF-IDF value calculation rule is as follows:
wherein
Is the keyword wiWord frequency in file f, N is the number of file sets, and
is to contain a keyword wiThe number of files of (c). Then, through POS algorithm (Parts of speech label)The context of the keyword is analyzed to obtain the part of speech of the keyword, and words with different parts of speech are restored through a word form restoring algorithm (Lemmatization), so that the restored words are accurate, unique and complete in semantic meaning.
In the keyword vector construction stage, the english keyword is first converted into a single character set, for example, the single character set corresponding to the keyword "present" is { r1，e1，p1，r2，e2，s1，e3，n1，t1In which r is1And r2Respectively representing the 1 st occurrence and the 2 nd occurrence of the character r in the keyword; then converting the single character set into a keyword vector with the size of 160 bits through a predefined conversion dictionary, wherein 26 x 5 bits represent letters, and 10 x 3 bits represent numbers and common symbols; if a bit of the keyword vector is 1, it means that the keyword contains a corresponding character, e.g. "r1"corresponds to the 17 th position" r2"corresponds to bit 43. In particular, since the keyword "representational" is different from its misspelled form "r 1 present" by only one element in the keyword vector, this is the key to our subsequent implementation of fuzzy queries using location sensitive hash functions.
In the construction process of a plaintext index tree, firstly, generating a file vector D for each file f, mapping the keyword vectors to obtain l hash values by using l independent P-stable LSH function trees for each keyword contained in the file f, wherein each hash value corresponds to one position in the file vector D, and the position is set as a TF-IDF value of the keyword in the file f; then storing the file vectors in leaf nodes, and constructing a keyword balanced binary tree structure from bottom to top, wherein the file vector contained in each intermediate node does not correspond to a certain file any more, but follows the following rules:
D[i]＝max{u.Pl→D[i],u.Pr→D[i]},i＝1,...,m
the P-stable LSH (P-stable locality sensitive hashing) family of functions, using the keyword vector as input, satisfies the following properties for any two points P and q:
if d(p,q)≤R:Pr[h(p)＝h(q)]≥P1
if d(p,q)≥cR:Pr[h(p)＝h(q)]≤P2
where h is an independent function in the LSH family of functions and d (p, q) represents the distance between point p and point q. This property ensures that keyword vectors with similar euclidean distances can hash to the same location in the document vector D with a high probability, while non-similar keyword vectors are mapped to the same location with a very small probability. Since the generation process of the query vector is similar to the generation process of the keyword vector in the index tree generation, but the corresponding bit is set to 1 (because the data user does not know the specific TF-IDF value of the query keyword), the cloud server can obtain the relevance score of the query request and the file f through the inner product operation of the vectors, thereby realizing the fuzzy ranking query.
(2) A verifiable fuzzy query index structure based on a Mercker tree is provided: in the encryption stage of the plaintext index tree, firstly, hashing a file digest by using a one-way low-collision HMAC function to obtain a fingerprint fp of a leaf node for each leaf node; then using the reversible matrix M according to the KNN encryption algorithm1And M2Encrypting a file vector D contained in each node in a plaintext index tree to obtain EncSK(D)＝{M1 T·D′,M2 TD' }, it is worth mentioning that the data user also encrypts the query vector in this way to obtain EncSK(Q)＝{M1 -1Q′,M2 -1Q' and according to the matrix multiplication rule:
M1 TD′·M1 -1Q′+M2 TD″·M2 -1Q″＝D′T·Q′+D″T·Q″＝DT·Q
therefore, the matrix encryption operation of the method on the file vector D and the query vector Q does not affect the calculation result of the correlation score, and a malicious attacker does not know the specific encryption matrix M1And M2The private information contained therein cannot be cracked by calculation.
For each intermediate node in the index tree, hashing character connections of fingerprints of left and right child nodes of the intermediate node by using the same HMAC function to obtain a fingerprint fp of the intermediate node; meanwhile, in order to make up for the defects of the merkel tree in the integrity check, each node in the encrypted index tree I also contains an authentication object VO, which is obtained by using the above HMAC function to perform fingerprint fp and encryption matrix Enc on the nodeSK(D) The character string connection is processed by Hash again to obtain the character string connection.
In the retrieval stage of the index tree, a search strategy based on a depth-first algorithm is used, for each node in the tree, the correlation score calculated by the search strategy and the query request is greater than the correlation scores of the child nodes on the left and right of the node, therefore, the search algorithm does not need to traverse the complete index tree, a returned result list is maintained, if the data user requests to return k files with the highest correlation, when the number of the files meeting the conditions in the result list is less than k, pruning operation is not carried out, leaf nodes which represent specific files and are traversed by the depth-first algorithm are stored in the result list and are arranged according to the correlation scores in a descending order; when the number of documents in the result list reaches k, if the relevance score of the current node is less than the lowest value in the result list, no more documents which are more in line with the query can be found in the subtree representing the node, the subtree is skipped by the search algorithm to improve the retrieval efficiency, which means that the leaf nodes traversed thereafter are more in line with the query requirement of the user than the k-th document in the result list, the search algorithm performs the replacement operation, and the descending order is still maintained.
Compared with the prior art, the technical scheme at least has the following remarkable effects:
1. the invention provides a fuzzy query search method based on a position-sensitive hash function, which is characterized in that keyword preprocessing is carried out based on a word form reduction algorithm, so that the tolerance of the fuzzy search algorithm on a time error and a spelling error is improved.
2. The invention provides a verifiable fuzzy query index structure based on a Mercker tree, which realizes the correctness verification of a returned result by constructing a balanced binary tree index structure and introducing a Mercker tree model, and simultaneously generates a verification object by using an HMAC function to carry out hash on an encryption matrix and node fingerprints stored in tree nodes, thereby overcoming the defect that the Mercker tree model cannot realize the integrity verification.
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FIG. 1 is a model diagram of a verifiable fuzzy search method based on a location sensitive hash function according to the present invention;
FIG. 2 is a diagram of an embodiment of a verifiable fuzzy search method based on a location sensitive hash function according to the present invention;
Detailed Description
The invention relates to a verifiable fuzzy search method based on a position sensitive hash function. For convenience of explanation, this embodiment describes a specific implementation of the present invention by taking a Request for Comments (RFC) data set as an example, but those skilled in the art should know that the technical solution of the present application does not limit the data type of the fuzzy search. These embodiments are merely to explain the technical principles of the present invention and are not intended to limit the scope of the present invention.
The method can be implemented according to the following steps, is not limited to any programming language and operating system, and in the example, a python programming language is taken as an example, and the model is built on a centros 7 operating system, and the specific steps are as follows:
the method comprises the following steps: and realizing a fuzzy search model based on outsourced encrypted data.
Fig. 1 is a model diagram of a verifiable fuzzy search method based on a location sensitive hash function, namely, a data owner, a cloud server, and a data user. Fig. 2 is a diagram of an embodiment of a verifiable fuzzy search method based on a location sensitive hash function according to the present invention. The data owner outsources the encrypted data to the cloud server for storage, authorized data users acquire an interested file set by sending encrypted queries to the cloud server, and the cloud server can realize verifiable multi-keyword fuzzy queries according to query requests of the data users on the premise of not knowing specific file contents.
Step two: data set preparation.
3200 files are arbitrarily selected from the RFC data set as sample data, 3400 keywords are extracted from the sample data, and in order to better verify the usability of the experiment, the number of the keywords contained in each file is ensured to be between 90 and 180 so as to simulate an experiment scene more complicated than an actual search environment.
Step three: and constructing an encryption index tree.
Firstly, preprocessing an extracted keyword set, and calculating to obtain a corresponding TF-IDF value as a search weight of the keyword in different files; then converting the keywords into keyword vectors with the length of 160 bits according to a preset dictionary; constructing a blank vector with the length of 8000 bits for each file in the data set as a file index, hashing the keyword vector contained in each file by using 8 independent hash functions in a p-stable lsh function family to obtain 8 positions for each file, and setting the positions as the search weight of the keyword corresponding to the file; constructing 3200 leaf nodes to store index vectors of all files, and then generating the rest nodes of the unencrypted index tree from bottom to top; then, encrypting vectors stored in the tree nodes by using a KNN encryption algorithm to obtain a matrix form; finally, the HMAC function is used for generating the fingerprint and the verification object of each node.
Step four: an encrypted query request is constructed.
Randomly selecting 10 keywords as query keywords, and replacing any letter in 2 keywords with other characters to simulate spelling errors; the construction of the encrypted query request is similar to the generation process of the file index vector, except that 8 positions obtained in the mapping stage are set to 1 instead of the weight of the keyword, and the query vector is encrypted to obtain a matrix form by using a KNN encryption algorithm.
Step five: and (6) testing.
And testing on the encrypted index tree structure constructed in the third step. The testing process specifically comprises the steps of calculating a query request and a node encryption matrix by adopting matrix operation to obtain a correlation score, then taking 5 files with the highest score as search results according to descending order, and simultaneously returning the encryption matrix stored in the path node on the search path, and taking the fingerprint and a verification object thereof as verification information of correctness and integrity to judge whether the cloud server has fraudulent behaviors.
In summary, aiming at the problem of multi-keyword fuzzy query of English documents, the invention provides a verifiable fuzzy search method based on a position sensitive hash function, which realizes the return of a most interesting file set to a user by mapping files and query requests into a matrix form and obtaining a query correlation score through matrix operation, and has practical application value in common scenes of query keyword spelling errors and cloud server fraud in the field of cloud computing.
It will be appreciated by persons skilled in the art that the scope of the present invention is not limited to the specific embodiments described. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and it is noted that the technical solutions after the changes or substitutions will fall within the protection scope of the invention.
Claims (2)
1. A fuzzy query search framework based on a location sensitive hash function, the method comprising:
the tense of the keyword is restored by using a morphological restoration algorithm according to the part of speech of the keyword, so that a satisfactory search result of a user can still be accurately returned when the spelling error or tense inconsistency of the query keyword occurs;
the verifiable fuzzy search method based on location sensitive hash function of claim 1, wherein the fuzzy query search framework based on location sensitive hash function specifically comprises:
and completing construction of the index tree through data preprocessing, keyword vector generation and position sensitive hash mapping. Extracting a keyword set in a data file in a data preprocessing stage, calculating word frequency-inverse text frequency of the keywords, and analyzing the parts of speech of the keywords according to context by a morphological reduction algorithm to realize simplification of the keywords; and in the keyword vector generation stage, according to the space vector model, the vector is used as a file index, and meanwhile, a position sensitive hash function is used for mapping the input keyword vector to complete the construction of a plaintext index tree.
2. A markov tree based verifiable fuzzy query indexing structure, the method comprising:
an index tree structure is built by combining a Mercker tree model, on the basis, a tree node signature is subjected to Hash again by using a one-way anti-collision HMAC function to generate a verification object, so that when a cloud service provider has a fraudulent behavior, the correctness and integrity of a returned result are verified;
the verifiable fuzzy search method based on the location sensitive hash function of claim 2, wherein the verifiable fuzzy query index structure based on the merkel tree specifically comprises:
in the index tree construction stage, file index vectors are encrypted by using a vector splitting and matrix encryption mode, file digests corresponding to leaf nodes are hashed by using a one-way anti-collision HMAC function to obtain node fingerprints, and the same HMAC function is used for performing re-hashing on the connection of left and right child fingerprints of tree nodes and encryption matrixes corresponding to the child fingerprints to generate verification objects; for each query, a greedy depth-first based search algorithm returns a set of verification objects, and a data user can verify the correctness and integrity of the returned search results by analyzing the set.
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