Fuzzy search method for protecting privacy of query party

Abstract

The invention belongs to the technical field of information security, and discloses a fuzzy search method for protecting privacy of a query party, which comprises the following steps of: encrypting a query keyword by the query party, and sending the encrypted query keyword to a data party; the data party processes the encrypted query keyword by using a fuzzy matching algorithm; establishing an encrypted data index; returning an encrypted search result; and decrypting and displaying the search result. According to the method, the information can be safely retrieved under the condition that the query content of the user is not leaked. According to the method, high-performance fuzzy search is realized through a homomorphic encryption technology and a fuzzy matching algorithm. And the search keyword of the query party is always in an encrypted state, so that the security of user privacy is ensured. And through a specially designed data index and an encrypted search result return mechanism, the search efficiency is ensured, and the security of the data content is also ensured. According to the method, the limitation of the traditional privacy information retrieval technology during fuzzy query processing is solved, the application flexibility of a search system is improved, and the actual application range of the search system is widened.

Description

Technical Field

[0001] The present invention belongs to the field of information security technology, and in particular relates to a fuzzy search method for protecting the privacy of a querying party. Background Art

[0002] Privacy Information Retrieval (PIR) is an important branch of information security that focuses on protecting the privacy of users when they perform data queries. The main goal of PIR is to retrieve information securely without revealing the content of the user's query. Traditional technologies mainly support precise queries, that is, users must know exactly what information they want to retrieve, which is impractical in many practical application scenarios. The core of these technologies is to ensure the security of data and the privacy of user queries, and avoid leaking sensitive information during data transmission or processing. Such as medical records, financial information, and personal identity data. The main goal of PIR is to retrieve information securely without revealing the content of the user's query. This is particularly important in fields such as medical, financial, and personal data.

[0003] The technology of fuzzy search method is an important extension of traditional PIR technology. This method not only protects user privacy, but also allows more flexible queries, even if the user is not completely sure about the information they want. In many cases, users may only remember part of the information or want to explore related information. Fuzzy search allows such non-exact queries, thereby better serving the actual needs of users. When designing fuzzy search methods, one of the key challenges is to maintain high performance and privacy protection while providing flexibility. This may involve complex algorithms and security protocols.

[0004] In recent years, some studies have begun to explore the combination of machine learning techniques with PIR. These studies attempt to improve the search efficiency and accuracy of PIR systems while protecting user privacy. In addition, as the amount of data increases, the demand for fuzzy matching also grows. These techniques allow users to search based on partial or approximate keywords while maintaining the privacy of the data. The latest research focuses on improving the efficiency of PIR systems. For example, researchers have explored different data index structures and query optimization algorithms to reduce query time and increase the speed of data retrieval.

[0005] Therefore, there is a significant technical challenge: how to improve the PIR system's ability to support fuzzy queries without sacrificing user privacy. This requires the development of new algorithms and methods that can not only handle inaccurate or partial keyword queries, but also maintain data security and response efficiency. Summary of the invention

[0006] The main goal of the present invention is to propose a new fuzzy search method to solve the technical problems of insufficient performance and efficiency of traditional privacy information retrieval (PIR) technology when processing fuzzy or partial matching queries. To achieve the above purpose, the specific technical solution of the present invention is as follows:

[0007] A fuzzy search method for protecting the privacy of a querying party comprises the following steps:

[0008] Step 1: The query party encrypts the query keyword, and after encryption, the query keyword is sent to the data party;

[0009] Step 2: The data party uses a fuzzy matching algorithm to process the encrypted query keywords;

[0010] Step 3: Create an encrypted data index;

[0011] Step 4: Return the encrypted search results;

[0012] Step 5: Decrypt the search results and display them.

[0013] Furthermore, in step 1, the querying party uses homomorphic encryption technology to encrypt the query keywords.

[0014] Furthermore, in step 2, the data party estimates the similarity between the encrypted data item and the encrypted query keyword by designing a homomorphic encryption fuzzy matching scoring system. The steps of designing the homomorphic encryption fuzzy matching scoring system are as follows:

[0015] Step 2.1, select a homomorphic encryption model based on the learning error problem;

[0016] Step 2.2, encrypting and configuring parameters of the feature vector generated by the homomorphic encryption model, wherein the parameters include key size and error rate;

[0017] For each element in the private data D that the query party wants to query, calculate its feature vector f → (D), the eigenvector f → (D) includes word frequency and context;

[0018] Apply the homomorphic encryption model with learning error problem to obtain the encrypted feature vector Enc(f → (D)), ensure that the encrypted feature vector Enc(f → (D)) preserves mathematical properties that can be used in subsequent homomorphic operations;

[0019] Step 2.3: Build index and query encryption:

[0020] Use data structures such as B-trees or hash tables to index encrypted feature vectors;

[0021] When querying, the query keyword Q is converted into an encrypted feature vector Enc(f → (Q));

[0022] Step 2.4, homomorphic encryption similarity calculation:

[0023] Design a homomorphic similarity function H, which can be used to calculate Enc(f → (D)) and Enc(f → (Q)) to generate an encrypted similarity score Enc(S);

[0024] Step 2.5: Scoring system implementation:

[0025] The encryption threshold Enc(T) is introduced to filter the scores, and only the scores Enc(S) above this threshold will be considered;

[0026] Use the homomorphic property in the learning error problem model to perform linear combination and comparison of encrypted scores;

[0027] Step 2.6, sorting of encryption results:

[0028] The sorting protocol is constructed using homomorphic properties, so that the sorting protocol can sort the encrypted scores without decryption;

[0029] Step 2.7, decryption and display of encryption results:

[0030] The querying party uses its private key to decrypt the returned encrypted scores and obtain the final similarity score list; the decrypted results are displayed and sorted from high to low by score.

[0031] Furthermore, step 3 specifically includes:

[0032] Step 3.1, extract a fixed set of features from each item in the database;

[0033] Step 3.2: Encrypt each feature vector using the linear homomorphic encryption scheme to obtain the homomorphic encrypted feature vector Enc(f → (D)) can be used for subsequent encryption operations;

[0034] Step 3.3, add multi-dimensional data processing based on the KD tree structure, and achieve the processing capability of supporting encrypted data through modification; use the linear homomorphic encryption scheme based on the learning error problem to encrypt the feature vector of each data item in the database;

[0035] Select an encrypted vector from the encrypted feature vector set as the root node, recursively select the split point according to the fixed or predefined dimension order, divide the data into two parts, and build subtrees respectively. For each encrypted feature vector, determine the subtree it should be inserted into based on the comparison result between its value in the selected dimension and the node, and recursively perform until a suitable insertion position is found;

[0036] Step 3.4: Insert the homomorphically encrypted feature vector of each data item into the index structure;

[0037] Step 3.5: Compare the query vector and the encrypted vector in the index through an algorithm;

[0038] Step 3.6, when the encrypted query vector is received, the index structure is traversed to find the most similar data item, and the search is completed by comparing the encrypted query vector with the encrypted feature vector of the data item in the index structure;

[0039] Step 3.7: Sort the results by homomorphic encryption according to the searched encryption similarity scores.

[0040] Furthermore, in step 4, the sorted encrypted results are sent to the querying party, and the querying party decrypts the results using the private key to obtain the similarity score and data item.

[0041] Furthermore, in step 5, after receiving the encrypted search results, the querying party uses the private key to decrypt the encrypted search results.

[0042] The present invention has the following advantages:

[0043] High privacy protection: Through full encryption processing, user queries are ensured not to be leaked, and the server cannot access unencrypted user data.

[0044] Efficient data processing: Utilize optimized encryption algorithms and index structures to significantly improve the speed and accuracy of processing fuzzy or partial match queries.

[0045] Flexibility and scalability: Supports various types of fuzzy queries, including spelling errors and synonym processing, and is suitable for a variety of business and scientific research scenarios.

[0046] The present invention not only improves the efficiency and effectiveness of data retrieval, but also provides strong protection for the privacy of the querying party, and promotes the application and development of privacy protection technology in the field of data retrieval. The present invention can retrieve information securely without leaking the user's query content. The present invention mainly realizes high-performance fuzzy search through homomorphic encryption technology and fuzzy matching algorithm. In the present invention, the search keywords of the querying party are always in an encrypted state, ensuring the security of user privacy. At the same time, through the specially designed data index and encrypted search result return mechanism, both the efficiency of the search and the security of the data content are guaranteed. In addition, the present invention also solves the limitations of traditional privacy information retrieval technology in processing fuzzy queries, and improves the application flexibility and practical application scope of the search system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0048] In order to better understand the purpose, technical solutions and functions of the present invention, Figure 1 , the present invention is further described in detail, but the present invention can be implemented in a variety of different ways as defined and covered by the claims. The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention, and the exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0049] Step 1: Encrypt the query keywords.

[0050] First, the query party needs to encrypt its query keywords to ensure that the user's search intent will not be exposed during the transmission process. Homomorphic encryption technology is used, which allows the data party to operate and calculate on encrypted data without decrypting the data. The specific content of homomorphic encryption is as follows:

[0051] (1) Design a lightweight fully homomorphic encryption algorithm named "Probabilistic Homomorphic Encryption Fuzzy Matching Algorithm" (PHEFMA), which can provide data parties with greater flexibility in processing ciphertext data. The following is the design framework:

[0052] (2) Key Generation (KeyGen):

[0053] Generate two large prime numbers p and q using a secure pseudo-random number generator (PRNG).

[0054] Calculate n = p × q, where n is part of the public key.

[0055] Choose appropriate noise distribution parameters to ensure the security and homomorphic properties of encryption.

[0056] (3) Encryption (Enc):

[0057] Represent the query keyword m as an integer.

[0058] Select random noise r and calculate the ciphertext c = (m + 2r) mod n.

[0059] The ciphertext c is a number modulo n that hides the message m.

[0060] (4) Homomorphic operation:

[0061] The algorithm is designed to allow addition and multiplication operations on the ciphertext c.

[0062] Addition: c1+c2modn corresponds to the addition of the plaintext.

[0063] Multiplication: c1×c2modn corresponds to the multiplication of the plaintext.

[0064] Step 2: Design of fuzzy matching algorithm.

[0065] After encryption, the query keywords are sent to the data party. The data party uses a lightweight fully homomorphic encrypted fuzzy matching algorithm to process these encrypted query keywords to support fuzzy matching. A homomorphic encryption fuzzy matching scoring system is designed to estimate the similarity between the encrypted data items and the encrypted query keywords. The technical content of the homomorphic encryption fuzzy matching scoring system is as follows:

[0066] (1) Basic Homomorphic Encryption Model Selection

[0067] Based on the consideration of balancing security and efficiency, a homomorphic encryption model based on the learning with error problem (LWE) is selected.

[0068] After selecting LWE as the homomorphic encryption model, parameters (including key size and error rate) are set for feature vector encryption to ensure that when encrypting the feature vector, neither the security of the encrypted data nor the mathematical computing power after encryption is affected.

[0069] (2) Feature vector encryption

[0070] For each element in the data item D, calculate its eigenvector f → (D), including word frequency, context, etc.

[0071] Apply the LWE homomorphic encryption algorithm to obtain Enc(f → (D)), ensuring that these cryptographic features retain mathematical properties that can be used in subsequent homomorphic operations.

[0072] (3) Building indexes and query encryption

[0073] Use data structures such as B-trees or hash tables to index encrypted feature vectors and optimize retrieval performance.

[0074] When querying, the query keyword Q is also converted into an encrypted feature vector Enc(f → (Q)).

[0075] (4) Homomorphic encryption similarity calculation

[0076] Design a homomorphic similarity function H, which can be used to quantify the similarity between two encrypted vectors Enc(f → (D)) and Enc(f → (Q)) to generate the encrypted similarity score Enc(S).

[0077] A homomorphic version of the vector dot product is used, which can be performed directly on the ciphertext due to its linear property.

[0078] (5) Implementation of the scoring system

[0079] The encryption threshold Enc(T) is introduced to filter the scores, and only the scores Enc(S) above this threshold will be considered.

[0080] The homomorphic property in the LWE model is used to perform linear combination and comparison of encrypted scores.

[0081] (6) Sorting of Encryption Results

[0082] Homomorphic properties are used to construct a secure sorting protocol, which makes it possible to sort the encrypted scores without decrypting them.

[0083] (7) Decryption and display of encryption results

[0084] The querying party uses its private key to decrypt the returned encrypted scores and obtain the final similarity score list.

[0085] Display the decrypted results, sorted by score from high to low.

[0086] The scoring system is designed based on a series of probability models and encrypted data features. The design of this algorithm is the key to the system, which needs to identify similar data items without decrypting the query keyword.

[0087] Step 3: Create an encrypted data index

[0088] In order to improve the search efficiency, a special index is established for the data party's database. The index is based on the encrypted form of the data item, allowing the system to quickly retrieve the results that fuzzily match the encrypted query keywords. This indexing method ensures both the efficiency of the search and the security of the database content.

[0089] The specific process involved in encrypting data indexes is as follows:

[0090] (1) Data Feature Extraction

[0091] A fixed set of features, such as term frequency (TF) and inverse document frequency (IDF), are extracted from each item in the database.

[0092] (2) Homomorphic Encryption of Feature Vectors

[0093] Each feature vector is encrypted using a linear homomorphic encryption scheme (an encryption scheme using LWE).

[0094] The homomorphic encryption feature vector Enc(f → (D)) can be used for subsequent encryption operations.

[0095] (3) Index Structure Establishment

[0096] (3.1) Index structure design and feature vector encryption

[0097] Based on the structure of KD tree, multi-dimensional data processing is added, and the processing capacity of supporting encrypted data is achieved through modification; the feature vector of each data item in the database is encrypted using the linear homomorphic encryption scheme based on LWE. Ensure that all data remains encrypted during the indexing and searching process to ensure data security. Since the encrypted data cannot be directly compared in size, a fixed dimension order can be selected or the segmentation dimension can be selected based on previous statistical data to facilitate data segmentation when building and querying indexes.

[0098] (3.2) Index creation

[0099] Select an encrypted vector from the encrypted feature vector set as the root node. Recursively select the split point in a fixed or predefined dimension order to divide the data into two parts and build subtrees respectively. For each encrypted feature vector, determine the subtree it should be inserted into based on the comparison result between its value in the selected dimension and the node (through the designed encrypted data comparison logic), and recursively perform until a suitable insertion position is found.

[0100] (4) Index Population

[0101] Insert the homomorphically encrypted feature vector of each data item into the index structure.

[0102] The tree structure is optimized according to the dimension of the encrypted feature vector to ensure search efficiency.

[0103] (5) Implementation of Homomorphic Encrypted Fuzzy Matching Algorithm

[0104] Implement an algorithm to compare the query vector with the encrypted vector in the index.

[0105] This algorithm can be a homomorphic encrypted dot product operation, which allows the similarity score to be calculated on the ciphertext.

[0106] (6) Index Searching

[0107] When an encrypted query vector is received, the index structure is traversed to find the most similar data items.

[0108] The search is done by comparing the encrypted query vector with the encrypted feature vector of the data item in the index structure.

[0109] (7) Homomorphic Encrypted Result Processing

[0110] According to the searched encryption similarity scores, the results are homomorphically encrypted and sorted.

[0111] A comparison operation implemented using homomorphic encryption technology determines which encrypted results best match the query.

[0112] Step 4: Encrypt the return of search results:

[0113] The sorted encrypted results are sent to the querying party, who uses the private key to decrypt the results and obtain the actual similarity score and data items.

[0114] When matching data is found, it needs to be encrypted and sent back to the querying party. This step ensures that even if the search results are intercepted during transmission, they cannot be understood or used by unauthorized parties.

[0115] Step 5: Decryption and display of results:

[0116] After receiving the encrypted search results, the querying party uses its private key to decrypt them. The decrypted data is the information required by the end user. This step ensures that the end user can safely receive and view the search results.

[0117] Through the above scheme, the present invention realizes a search system that supports fuzzy search and protects user privacy. The user's search keywords are encrypted from beginning to end, protecting the user's privacy information from being leaked. At the same time, through special algorithm and data structure design, the efficiency and accuracy of the search are guaranteed.

[0118] The present invention is a novel fuzzy search method for protecting the privacy of the querying party, which allows users to perform fuzzy search through encryption technology, especially homomorphic encryption, even if the keywords queried by the user are not completely or partially matched. It involves homomorphic encryption application, fuzzy matching algorithm, encrypted data index and encrypted result transmission.

[0119] For homomorphic encryption applications: Through mathematical construction, it allows calculations to be performed on encrypted data, and ensures that the decrypted ciphertext of the calculation result is the same as the direct calculation result of the plaintext. Homomorphic encryption allows operations such as sorting and matching to be performed on encrypted data without decryption, thereby protecting the privacy of the query content.

[0120] For fuzzy matching algorithms: Combined with natural language processing technology, it can handle imprecise queries by analyzing semantic and contextual information. Using probability models and text analysis technology, the algorithm can identify and handle spelling errors, synonyms, and semantically similar words.

[0121] For encrypted data indexes: Use special data structures, such as inverted indexes and B-trees, which are optimized to support efficient searches of encrypted data. Create indexes to support fast fuzzy matching, allowing the system to quickly locate and return results that fuzzily match the query criteria.

[0122] For encrypted result transmission: Encryption protocols (such as TLS / SSL) are used to ensure the confidentiality and integrity of data transmitted over public networks. Search results are encrypted before being returned to users to protect the privacy and security of data during transmission.

[0123] The specific application of the method of the present invention is described below through a specific embodiment.

[0124] Application scenarios:

[0125] xx network security data management. In the context of xx network security, processing and managing large amounts of sensitive data is crucial. The fuzzy search method of the present invention aims to improve the efficiency of data retrieval while ensuring data security and privacy protection.

[0126] System Configuration:

[0127] Encrypted communication network: Build a highly encrypted communication network to ensure that all data transmissions are encrypted with strict security protocols.

[0128] Central Control Server: A high-performance central server is set up to receive, process and respond to query requests from within xx. The server uses advanced fuzzy matching algorithms and integrates the latest encryption technology.

[0129] User terminal interface: The terminal device used by xx members is equipped with specially designed query software for generating and sending encrypted query requests.

[0130] Operation process:

[0131] Query request encryption: xx members initiate fuzzy query requests on terminal devices, such as partial information or incomplete identifiers for specific security events. The query request is encrypted by the terminal software before being sent.

[0132] Central server processing: Encrypted queries are transmitted to a central server over a secure network. The server processes these queries using a specialized fuzzy matching algorithm to effectively locate relevant information in large amounts of sensitive data.

[0133] Result encryption and transmission: Once the matching data is located, the central server encrypts the result and sends it back to the requesting terminal through an encrypted communication network.

[0134] Terminal result decryption and display: The terminal device receives the encrypted search results, decrypts them and displays them to the operator to ensure the confidentiality and integrity of the information.

[0135] The present invention can produce the following beneficial technical effects in improving the existing privacy information retrieval (PIR) technology:

[0136] 1. Enhanced query flexibility and accuracy: By supporting fuzzy search, the present invention can significantly improve query flexibility. Users can search based on imprecise keywords or fuzzy conditions, thereby obtaining more extensive and relevant search results. This is particularly useful when processing complex or incomplete query requests, improving overall search efficiency and user experience.

[0137] 2. Expanded application scope: The flexible search function of the present invention makes it applicable to a wider range of application scenarios. In particular, in areas that require in-depth data analysis and flexible information retrieval, such as business intelligence, medical information systems, and academic research, the present invention can provide richer data insights.

[0138] 3. Improve processing speed: In view of the performance problems of existing technologies in processing large-scale data sets, the technical solution proposed in this invention can significantly improve the speed of data processing. This is especially important in scenarios that require real-time data processing and rapid response.

[0139] 4. Maintain high-level data privacy and security: While improving functionality, the present invention still emphasizes data privacy and security protection. This means that even when conducting more complex fuzzy searches, the user's query content and personal data are fully protected.

[0140] 5. Reduce data processing costs: By optimizing algorithms and data processing procedures, the present invention can reduce the overall cost of data processing. This is particularly beneficial for organizations that need to process large amounts of data, and can effectively reduce the burden on hardware resources and computing resources.

[0141] Compared with the prior art, the present invention can bring significant beneficial effects in terms of improving search flexibility, accelerating processing speed, expanding application scope, protecting data privacy and reducing costs. These improvements not only optimize the user experience, but also provide more efficient and secure data processing solutions for data-intensive industries.

[0142] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A fuzzy search method for protecting the privacy of the querying party, characterized in that: The steps include: Step 1: The query party encrypts the query keyword, and after encryption, the query keyword is sent to the data party; Step 2: The data party uses a fuzzy matching algorithm to process the encrypted query keywords; Step 3: Create an encrypted data index; Step 4: Return the encrypted search results; Step 5: Decrypt the search results and display them.

2. A fuzzy search method for protecting the privacy of the querying party according to claim 1, characterized in that: In step 1, the querying party uses homomorphic encryption technology to encrypt the query keywords.

3. A fuzzy search method for protecting the privacy of the querying party according to claim 2, characterized in that: In step 2, the data party estimates the similarity between the encrypted data item and the encrypted query keyword by designing a homomorphic encryption fuzzy matching scoring system. The steps of designing the homomorphic encryption fuzzy matching scoring system are as follows: Step 2.1, select a homomorphic encryption model based on the learning error problem; Step 2.2, encrypting and configuring parameters of the feature vector generated by the homomorphic encryption model, wherein the parameters include key size and error rate; For each element in the private data D that the query party wants to query, calculate its feature vector f → (D), the eigenvector f → (D) includes word frequency and context; Apply the homomorphic encryption model with learning error problem to obtain the encrypted feature vector Enc(f → (D)), ensuring that the encrypted feature vector Enc(f→(D)) retains mathematical properties that can be used in subsequent homomorphic operations; Step 2.3: Build index and query encryption: Use data structures such as B-trees or hash tables to index encrypted feature vectors; When querying, the query keyword Q is converted into an encrypted feature vector Enc(f → (Q)); Step 2.4, homomorphic encryption similarity calculation: Design a homomorphic similarity function H, which can be used to calculate Enc(f → (D)) and Enc(f → (Q)) to generate an encrypted similarity score Enc(S); Step 2.5: Scoring system implementation: The encryption threshold Enc(T) is introduced to filter the scores, and only the scores Enc(S) above this threshold will be considered; Use the homomorphic property in the learning error problem model to perform linear combination and comparison of encrypted scores; Step 2.6, sorting of encryption results: The sorting protocol is constructed using homomorphic properties, so that the sorting protocol can sort the encrypted scores without decryption; Step 2.7, decryption and display of encryption results: The querying party uses its private key to decrypt the returned encrypted scores and obtain the final similarity score list; the decrypted results are displayed and sorted from high to low by score.

4. A fuzzy search method for protecting the privacy of the querying party according to claim 3, characterized in that: Step 3 specifically includes: Step 3.1, extract a fixed set of features from each item in the database; Step 3.2: Encrypt each feature vector using the linear homomorphic encryption scheme to obtain the homomorphic encrypted feature vector Enc(f → (D)) can be used for subsequent encryption operations; Step 3.3, add multi-dimensional data processing based on the KD tree structure, and achieve the processing capability of supporting encrypted data through modification; use the linear homomorphic encryption scheme based on the learning error problem to encrypt the feature vector of each data item in the database; Select an encrypted vector from the encrypted feature vector set as the root node, recursively select the split point according to the fixed or predefined dimension order, divide the data into two parts, and build subtrees respectively. For each encrypted feature vector, determine the subtree it should be inserted into based on the comparison result between its value in the selected dimension and the node, and recursively perform until a suitable insertion position is found; Step 3.4: Insert the homomorphically encrypted feature vector of each data item into the index structure; Step 3.5: Compare the query vector and the encrypted vector in the index through an algorithm; Step 3.6, when the encrypted query vector is received, the index structure is traversed to find the most similar data item, and the search is completed by comparing the encrypted query vector with the encrypted feature vector of the data item in the index structure; Step 3.7: Sort the results by homomorphic encryption according to the searched encryption similarity scores.

5. A fuzzy search method for protecting the privacy of the querying party according to claim 4, characterized in that: In step 4, the sorted encrypted results are sent to the querying party, and the querying party decrypts the results using the private key to obtain the similarity score and data item.

6. A fuzzy search method for protecting the privacy of the querying party according to claim 5, characterized in that: In step 5, after receiving the encrypted search results, the querying party uses the private key to decrypt the encrypted search results.

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