Cloud data processing method and system based on chaos test platform, medium and equipment

Abstract

The invention provides a cloud data processing method and system based on a chaos test platform, a medium and equipment, and the method comprises the steps: carrying out the local encryption of the data of a user through employing a data processing method based on homomorphic encryption, and uploading the encrypted data to a cloud platform for processing. The cloud platform calculates and analyzes the data in the encrypted state, and does not need to decrypt the data, so that the security and privacy of the data are protected. And before the calculation result is returned to the user, the result can be decrypted by using the secret key, and the decrypted result is verified, so that the integrity and credibility of the data are ensured. By using the homomorphic encryption algorithm, the security and privacy of user data can be protected to the greatest extent in the cloud data processing process.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud data processing, and in particular relates to a cloud data processing method, system, medium and equipment based on a chaos testing platform. Background Art

[0002] Cloud data processing technology emerged against the backdrop of the rapid development of information technology and the explosive growth of data. With the popularization of the Internet and the widespread use of various digital devices, a large amount of data is generated and stored, which contains rich information and value. Traditional data processing methods can no longer meet the needs of efficient processing and analysis of massive data, so cloud data processing technology came into being.

[0003] Cloud data processing technology is a new data processing method that migrates data storage and processing tasks to cloud platforms. Cloud platforms have powerful computing and storage capabilities and can flexibly allocate resources to adapt to data processing tasks of different scales and requirements. By uploading data to the cloud platform, users can process, analyze and mine data in the cloud environment, making full use of the advantages of cloud computing and distributed systems.

[0004] The development of cloud data processing technology benefits from the following key factors. First, the rise of cloud computing provides the infrastructure and support for cloud data processing technology. Cloud computing centrally manages and schedules computing, storage and network resources through virtualization and resource pooling, providing a highly flexible and scalable computing environment. Second, the rise of big data has promoted the development of cloud data processing technology. Big data is characterized by huge data volume, diverse types and fast speed, and traditional data processing methods are no longer competent. Cloud data processing technology can efficiently process large-scale data and discover the value and insights in the data through distributed computing and parallel processing. In addition, cloud data processing technology also benefits from the rapid development of machine learning and artificial intelligence. Machine learning and artificial intelligence algorithms require a large amount of data for training and learning. Cloud data processing technology provides an efficient data processing and analysis platform, which provides support for the application and promotion of algorithms.

[0005] Cloud data processing technology has a wide range of application scenarios. In the business field, cloud data processing technology can be applied to marketing, customer relationship management, supply chain management, etc., to help companies conduct data analysis and decision support. In the financial field, cloud data processing technology can help banks conduct risk assessment and transaction analysis. In the medical and health field, cloud data processing technology can be used for disease prediction and genome analysis. In transportation and urban planning, cloud data processing technology can be used for traffic optimization and intelligent management. In the manufacturing industry, cloud data processing technology can be used for quality control and supply chain optimization. In short, cloud data processing technology provides efficient, flexible and scalable data processing solutions for all walks of life.

[0006] However, cloud data processing technology also faces some challenges and problems. One of them is data security and privacy issues. Since data storage and processing are carried out on cloud platforms, data security and privacy face potential risks. Hacker attacks, data leaks, and unauthorized data access may lead to the leakage of user sensitive information and the violation of privacy rights. In addition, cloud data processing technology also faces challenges in data transmission and network bandwidth limitations, as well as data quality and consistency control.

[0007] In summary, cloud data processing technology has important significance and application value in the era of big data. It provides efficient, flexible and scalable data processing solutions for all walks of life by making full use of the advantages of cloud computing and distributed systems. However, cloud data processing technology still faces some challenges and problems, such as data security and privacy issues. Since cloud data processing technology needs to store data on a cloud platform and usually involves multiple data processing links, such as data transmission, storage, computing and analysis, data security and privacy face potential risks. Hacker attacks, data leaks and unauthorized data access may lead to the leakage of user sensitive information and the infringement of privacy rights. Summary of the invention

[0008] The present invention aims at the insufficiency of cloud data processing technology based on chaos test platform in data security problem, and provides a cloud data processing method, system, medium and device based on chaos test platform. Using the data processing method based on homomorphic encryption, the user's data is encrypted locally, and then the encrypted data is uploaded to the cloud platform for processing. The cloud platform calculates and analyzes the data in an encrypted state without decrypting the data, thereby protecting the security and privacy of the data. Before the calculation result is returned to the user, the result can be decrypted using a key, and the decrypted result can be verified to ensure the integrity and credibility of the data. By using the homomorphic encryption algorithm, the security and privacy of user data can be protected to the maximum extent during the cloud data processing process.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] A cloud data processing method based on a chaos testing platform, characterized by comprising:

[0011] Perform homomorphic searchable encryption on the data locally and build the corresponding encryption index;

[0012] Upload the encrypted data and index to the cloud platform through a secure channel and store them in encrypted form;

[0013] The cloud platform receives the encrypted search request, performs the search operation in an encrypted state, and returns the encrypted search results;

[0014] Use the key to decrypt the encrypted search results, obtain the plaintext data, and perform data integrity verification and data credibility verification;

[0015] Convolutional neural networks or long short-term memory networks are used on the decrypted plaintext data to extract information.

[0016] To optimize the above technical solutions, the specific measures taken also include:

[0017] Furthermore, the encrypted index is a BHB index that combines a B-tree encrypted index, a hash encrypted index, and a Bitrmap encrypted index.

[0018] Furthermore, the BHB index uses a B-tree as an index structure, the B-tree includes multiple nodes, each node includes one or more encrypted keywords and a pointer to a child node; the nodes of the B-tree encrypt the keywords using a homomorphic searchable encryption algorithm, and are adjusted according to the insertion, deletion and balance operations of the B-tree;

[0019] The BHB index uses a hash function to convert each keyword into a unique address, and uses the address as the keyword of the B-tree node; the hash function uses a homomorphic searchable encryption algorithm to encrypt the keyword and calculates the address of the keyword in the encrypted state;

[0020] The BHB index utilizes a Bitmap index to maintain a bit vector for each possible keyword value, each bit of the bit vector corresponds to a document or data record, and the bit vector is operable in an encrypted state.

[0021] Furthermore, the cloud platform uses a homomorphic encryption algorithm based on the Paillier encryption system to perform calculations, matches the search request with the encrypted index, and returns encrypted search results.

[0022] Furthermore, the homomorphic encryption algorithm based on the Paillier encryption system includes a key generation phase, an encryption phase and a decryption phase;

[0023] In the key generation phase, two prime numbers p and q are selected, and the modulus n=pq and λ=lcm(p-1,q-1) are calculated; a random integer g is selected so that g belongs to the multiplication group of n^2; the modular inverse element μ=(L(g^λmod n^2))^(-1)mod n is calculated, where L(u)=(u-1) / n; then the public key is (n, g) and the private key is (λ, μ);

[0024] In the encryption phase, given the plaintext m and the public key (n, g), the ACE algorithm is used for encryption;

[0025] In the decryption phase, given the ciphertext c and the private key (λ, μ), the ACE algorithm is used for decryption.

[0026] Furthermore, the encryption using the ACE algorithm is specifically as follows: selecting a random number r, calculating a ciphertext c = (g^m*r^n mod n^2)*Enc(1,n)^r mod n^2, wherein Enc(1,n) is the result of encrypting the plaintext;

[0027] The decryption using the ACE algorithm is specifically as follows: calculating the plaintext m=(L(c^λmod n^2)*μmod n-1) / n.

[0028] Further, the data integrity verification is achieved by using a message authentication code or a digital signature;

[0029] The data authenticity verification is achieved by using a digital certificate.

[0030] Accordingly, the present invention proposes a cloud data processing system based on a chaos testing platform, which is characterized by comprising:

[0031] Data encryption and index building module, used to perform homomorphic searchable encryption on data locally and build corresponding encrypted indexes;

[0032] Encrypted data upload and storage module, used to upload encrypted data and indexes to the cloud platform through a secure channel and store them in encrypted form;

[0033] Homomorphic search module, used to receive encrypted search requests, perform search operations in an encrypted state, and return encrypted search results;

[0034] A decryption and verification module is used to decrypt the encrypted search results using a key, obtain the plaintext data, and perform data integrity verification and data credibility verification;

[0035] The data processing and analysis module is used to extract information using convolutional neural networks or long short-term memory networks on decrypted plaintext data.

[0036] Accordingly, the present invention proposes a computer-readable storage medium storing a computer program, characterized in that the computer program enables a computer to execute the cloud data processing method based on the chaos testing platform as described above.

[0037] Accordingly, the present invention proposes an electronic device, characterized in that it includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the cloud data processing method based on the chaos testing platform as described above is implemented.

[0038] The beneficial effects of the present invention are:

[0039] 1) Aiming at the data security problem, the present invention adopts homomorphic encryption algorithm in cloud data processing technology based on cloud virtualization platform. In traditional methods, data usually needs to be decrypted before calculation and processing, which increases the risk of data being obtained by attackers during transmission and processing. Homomorphic encryption algorithm allows calculation operations on data in an encrypted state without decrypting the data, thereby protecting the security and privacy of data to the greatest extent.

[0040] 2) The present invention combines homomorphic encryption with searchable functions, which not only protects the security and privacy of data, but also provides flexible search capabilities. Traditional homomorphic encryption schemes mainly focus on data calculations, while the present invention introduces homomorphic searchable encryption, which enables users to perform data search operations in an encrypted state, improving the flexibility and efficiency of data queries. The present invention not only encrypts and protects data, but also implements secure search of encrypted data through homomorphic encryption algorithms, avoiding the risk of data privacy leakage during the search process. By performing further data processing and analysis on the decrypted plaintext data, users can make full use of the computing resources of the cloud platform to discover the potential value in the data.

[0041] 3) The present invention proposes a BHB design based on homomorphic searchable encryption, and uses the ACE algorithm to improve the Paillier encryption system based on BHB. The design combines homomorphic searchable encryption and BHB index construction to achieve the search and indexing functions of encrypted data. By using the ACE algorithm to improve the Paillier encryption system, an efficient additive homomorphic encryption scheme is provided, allowing users to perform search operations in an encrypted state to protect the privacy of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The present invention is a flow chart of a cloud data processing method based on a chaos testing platform.

[0043] Figure 2 It is a framework diagram of a cloud data processing system based on a chaos testing platform. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0045] In one embodiment, the present invention proposes a cloud data processing method based on a chaos testing platform, the process is as follows: Figure 1 The specific steps include the following steps.

[0046] S101: Data encryption and index construction.

[0047] Users perform homomorphic searchable encryption on data locally and build a corresponding encrypted index, which contains keywords or features of the data and is used for search operations in an encrypted state.

[0048] Homomorphic Searchable Encryption (HSE) is a technology that protects data privacy. It allows users to search and query data while it is encrypted. The goal of HSE is to ensure data privacy without affecting the availability and accessibility of data. Encrypted indexes are a key part of searchable encryption. They contain keywords or features of the data and are used for search operations in an encrypted state. The design of encrypted indexes must take into account two requirements: on the one hand, to ensure the security of encrypted data, and on the other hand, to support effective search operations.

[0049] In the cloud data processing of this embodiment, in order to realize the index structure in an encrypted state, the three encryption indexing methods of B-tree, hash and Bitmap can be combined to form a new comprehensive method, called BHB (B-Hash-Bitmap).

[0050] BHB index is an encrypted index structure that combines the features of B-tree, hash and bitmap. It has the multi-layer structure and range query capability of B-tree index, the fast search feature of hash index, and the advantages of efficient storage and processing of low cardinality data of Bitmap index.

[0051] Specifically, the BHB index can be constructed and operated in the following ways:

[0052] B-tree part:

[0053] The BHB index uses the B-tree as the main index structure and contains multiple nodes. Each node contains one or more encrypted keywords and pointers to child nodes.

[0054] The nodes of the B-tree can use a homomorphic searchable encryption algorithm to encrypt keywords to ensure the privacy of the data.

[0055] The nodes of the B-tree can be adjusted according to the insertion, deletion, and balance operations of the B-tree to maintain the structure and performance of the index.

[0056] Hash part:

[0057] The BHB index uses a hash function to convert each keyword into a unique address and uses the address as the keyword of the B-tree node.

[0058] The hash function can encrypt keywords using a homomorphic searchable encryption algorithm to protect the privacy of the data.

[0059] The hash function can quickly calculate the address of the keyword in an encrypted state, thereby achieving fast search operations.

[0060] Bitmap part:

[0061] The BHB index can utilize the idea of ​​Bitmap index and maintain a bit vector for each possible keyword value.

[0062] Each bit of the bit vector corresponds to a document or data record, and the bit vector can be operated on in an encrypted state.

[0063] The bit vector can indicate whether a data record contains the corresponding keyword information, supporting efficient low-cardinality data query and processing.

[0064] Combining the above three parts, the BHB index can support range queries, sorting operations, and fast search and processing of low-cardinality data in an encrypted state. By using homomorphic searchable encryption algorithms to protect data privacy, the BHB index provides a secure and efficient data indexing solution.

[0065] This embodiment further proposes a BHB (B-Hash-Bitmap) design based on homomorphic searchable encryption, which can realize the search and index construction of encrypted data in cloud data processing scenarios.

[0066] Data Encryption:

[0067] Encryption function: Enc(data, key) / / encrypt data

[0068] Decryption function: Dec(ciphertext, key) / / Decrypt the encrypted data

[0069] B-tree part:

[0070] Encrypted B-tree index construction:

[0071] B-tree node keyword encryption: EncKeyword = Enc(keyword, key) / / Use encryption function to encrypt the keyword

[0072] B-tree node pointer: Pointer / / pointer to child node

[0073] Hash part:

[0074] Hash function: H(keyword) / / Hash the keyword

[0075] Hash encryption index construction:

[0076] Hash value encryption: EncHash = Enc(H(keyword), key) / / Use encryption function to encrypt hash value

[0077] Bitmap part:

[0078] Bit vector construction:

[0079] Bitmap bit vector: BitVector / / The bit vector is used to indicate the presence or absence of a keyword

[0080] Bit operation: SetBit(BitVector, index) / / Set the specified bit in the bit vector to 1

[0081] Bit operation: ClearBit(BitVector, index) / / Clear the specified bit in the bit vector

[0082] Based on the above formulas, the mathematical derivation of BHB design is as follows:

[0083] BHB(Key)=EncKeyword+Pointer+EncHash+BitVector

[0084] BHB(Key): Encrypted keyword in BHB index

[0085] EncKeyword: The value of the keyword encrypted using the encryption function

[0086] Pointer: Pointer to the child node

[0087] EncHash: The value after encrypting the hash value using an encryption function

[0088] BitVector: A bit vector used to indicate the presence or absence of a keyword

[0089] BHB design implements the construction of encrypted B-tree index, encrypted hash index and encrypted bit vector index. Users can implement these formulas according to specific encryption algorithms and hash functions, and combine the characteristics of the chaos test platform to complete the process of data encryption and index construction.

[0090] The BHB design based on homomorphic searchable encryption provides encrypted data search and index building capabilities in cloud data processing scenarios. In this design, data is first encrypted by an encryption function and then decrypted using a decryption function.

[0091] The BHB design covers three parts: B-tree, hash, and Bitmap. In the B-tree part, the keyword is encrypted as EncKeyword, while retaining the pointer to the child node. In the hash part, the keyword is processed by the hash function to obtain the hash value H(keyword), and then the hash value is encrypted using the encryption function to obtain EncHash. In the Bitmap part, the bit vector BitVector is used to indicate the presence or absence of the keyword, and the bit vector is updated by setting and clearing bits.

[0092] Based on the above formula, BHB(Key) represents the encrypted keyword in the BHB index, which is composed of the encrypted keyword EncKeyword, the pointer Pointer pointing to the child node, the encrypted hash value EncHash and the bit vector BitVector.

[0093] The above content can realize the search and index construction of encrypted data. Users can implement these formulas according to specific encryption algorithms and hash functions, and combine the characteristics of the chaos test platform to realize the encryption and index construction process of data. Through the BHB design, users can safely perform data search and index operations in cloud data processing scenarios. Encryption protects the privacy of data while providing efficient search and indexing functions, allowing the chaos test platform to safely process encrypted data.

[0094] S102: Encrypted data upload and storage.

[0095] The encrypted data and indexes are uploaded to the cloud platform for storage through a secure channel. On the cloud platform, data is stored in encrypted form to ensure the security of data during transmission and storage. Once the data and indexes are encrypted, they can be uploaded to the cloud platform for storage through a secure channel. On the cloud platform, data is stored in encrypted form to ensure the security of data during transmission and storage.

[0096] In the BHB design based on homomorphic searchable encryption, the encrypted data and index are uploaded to the cloud platform through a secure channel for storage. In order to ensure the security of data during transmission and storage, encryption algorithms can be used to encrypt data and ensure the use of secure channels. On the cloud platform, data is stored in an encrypted form to ensure the security of data during transmission and storage.

[0097] By encrypting data through encryption functions, the original data can be converted into encrypted ciphertext, thereby ensuring the confidentiality and privacy of the data. Uploading the encrypted data to the cloud platform through a secure channel can prevent unauthorized access and tampering of the data during transmission.

[0098] Once the encrypted data and indexes are stored on the cloud platform, they will be saved in encrypted form. This means that even if the physical storage media of the cloud platform is accessed without authorization, the attacker cannot obtain the original data content. Only users who have the decryption key can decrypt the data and obtain the plaintext information.

[0099] By using a homomorphic searchable encryption method based on BHB, cloud platforms can provide data storage services while ensuring the confidentiality and privacy protection of user data. Even if the data and indexes stored on the cloud platform are stolen by attackers, since the data is stored in an encrypted form, the attacker cannot obtain the original data. Only authorized users can decrypt the data by using the decryption key to obtain the plaintext information.

[0100] This BHB-based homomorphic searchable encrypted cloud data processing solution can provide secure and efficient data storage and processing capabilities on the chaos test platform. Users can safely upload encrypted data and indexes to the cloud platform, and use the computing and storage resources of the cloud platform to perform data analysis, processing and query operations while protecting the privacy and confidentiality of the data. This solution achieves efficient data processing and storage while protecting data privacy, providing users with secure and reliable cloud data processing services.

[0101] In cloud data processing scenarios, the BHB design based on homomorphic searchable encryption provides strong data security and privacy protection. Suppose a medical institution wants to upload a patient's medical data to a cloud platform for storage and analysis. In order to protect the patient's privacy, the medical institution can use the BHB design to encrypt and index the medical data.

[0102] First, the medical institution encrypts the patient's medical data. Use encryption functions to encrypt sensitive medical data to ensure the security of the data during transmission. The encrypted data and the corresponding encrypted index can be uploaded to the cloud platform through a secure channel for storage.

[0103] On the cloud platform, encrypted medical data is stored in encrypted form to ensure the security of data during transmission and storage. Even if the physical storage medium of the cloud platform is accessed without authorization, the attacker cannot obtain the original medical data content. Only medical institutions with the decryption key can decrypt the data and obtain the plaintext medical information.

[0104] When medical institutions need to search and analyze data on the cloud platform, they can send encrypted search requests to the cloud platform. The cloud platform uses homomorphic encryption algorithms to match encrypted search requests with encrypted indexes and returns encrypted search results. After receiving the encrypted search results, users use the key to decrypt the results and obtain the plaintext results.

[0105] S103: Homomorphic search.

[0106] Users can perform search operations in an encrypted state by sending encrypted search requests to the cloud platform. The cloud platform uses homomorphic encryption algorithms to perform calculations, match search requests with encrypted indexes, and return encrypted search results.

[0107] Homomorphic encryption algorithm-Mathematical derivation of the Paillier encryption system:

[0108] Key Generation:

[0109] Choose two large prime numbers p and q so that gcd(pq, (p-1)(q-1)) = 1. This ensures that the modulus n = pq and λ = lcm(p-1, q-1) are relatively prime.

[0110] Choose a random integer g such that g belongs to the multiplicative group of n^2.

[0111] Calculate the modular inverse element μ of n = (L(g^λ mod n^2))^(-1) mod n, where L(u) = (u-1) / n.

[0112] The public key is (n, g) and the private key is (λ, μ).

[0113] Encryption: Given a plaintext m and a public key (n, g), calculate the ciphertext c = g^m*r^n mod n^2, where r is randomly chosen.

[0114] Decryption: Given the ciphertext c and the private key (λ, μ), calculate the plaintext m = L(c^λmodn^2)*μmodn.

[0115] This system is additively homomorphic because for any two plaintexts m1 and m2, and their ciphertexts c1 and c2, we have:

[0116] c1*c2 mod n^2=(g^ml*r1^n*g^m2*r2^n)mod n^2=g^(m1+m2)*(r1*r2)^n modn^2

[0117] The last expression is a valid encryption of ml+m2. The Paillier encryption system is a public key encryption system that protects the privacy of data while allowing certain computations to be performed on the encrypted data.

[0118] In the homomorphic searchable encryption scheme based on the Paillier encryption system, users can perform search operations in an encrypted state by sending encrypted search requests to the cloud platform. The cloud platform uses the Paillier encryption algorithm to calculate the search request, match it with the encrypted index, and return the encrypted search results.

[0119] In the Paillier encryption system, the key generation process includes selecting two large prime numbers p and q, ensuring that their product pq is relatively prime to the Euler function (p-1)(q-1). Then a random integer g is selected to belong to the multiplication group of the modulus n^2. By calculating the modular inverse element μ of the modulus n and defining the function L(u) = (u-1) / n, the public key (n, g) and private key (λ, μ) are generated.

[0120] During the encryption process, the user takes the plaintext m and the public key (n, g) as input and calculates the ciphertext c. The specific calculation formula is c = g^m*r^nmodn^2, where r is a randomly selected value. In this way, each encryption of the same plaintext will generate a different ciphertext, which increases the security of encryption.

[0121] During the decryption process, the user takes the ciphertext c and the private key (λ, μ) as input and calculates the plaintext m. The decryption calculation formula is m = L (c^λ modn^2) * μ modn. Through this formula, the plaintext content before encryption can be restored.

[0122] The Paillier encryption system has an additive homomorphic property. For any two plaintexts ml and m2, and their ciphertexts c1 and c2, when the two ciphertexts are multiplied and modulo n^2, the result obtained corresponds to the encryption result of the plaintext ml+m2. This means that certain search operations can be performed in the encrypted state without decrypting the data, thereby maintaining the privacy of the data.

[0123] This homomorphic searchable encryption scheme is of great significance in cloud data processing scenarios. It allows users to send encrypted search requests to the cloud platform for calculation and matching while protecting data privacy, and obtain encrypted search results. This provides users with a secure and efficient cloud data search service. Through the additive homomorphic properties of the Paillier encryption system, users can perform search operations in an encrypted state without exposing plaintext data, thus protecting the privacy and confidentiality of the data.

[0124] The homomorphic searchable encryption scheme based on the Paillier encryption system provides a secure and efficient search service for cloud data processing. Through the encryption algorithm and the additive homomorphic property, users can perform search operations in an encrypted state while protecting the privacy of the data. This scheme has important innovation and value in the chaos test platform, providing users with a safer and more convenient data processing and search experience.

[0125] In the BHB design based on homomorphic searchable encryption, the ACE (Additive Cryptosystem for Effficiency) algorithm can be further used to upgrade the Paillier encryption system. ACE is an additive homomorphic encryption scheme that can perform efficient addition operations on encrypted data.

[0126] First, in the key generation phase, select two large prime numbers p and q, and calculate the modulus n = pq and λ = lcm(p-1, q-1). Select a random integer g so that g belongs to the multiplication group of n^2. Calculate the modular inverse element μ = (L(g^λmod n^2))^(-1)mod n, where L(u) = (u-1) / n. The public key is (n, g) and the private key is (λ, μ).

[0127] In the encryption phase, given the plaintext m and the public key (n, g), the ACE algorithm is used for encryption. First, a random number r is selected and the ciphertext c = (g^m*r^n mod n^2)*Enc(1, n)^r mod n^2 is calculated, where Enc(1, n) is the result of encrypting the plaintext 1.

[0128] In the decryption phase, given the ciphertext c and the private key (λ, μ), the ACE algorithm is used for decryption. The plaintext m is calculated as (L(c^λ mod n^2)*μ mod n-1) / n.

[0129] Through this improvement, the ACE algorithm was introduced on the basis of the Paillier encryption system, making the addition operation more efficient. The ACE algorithm introduces the random power operation of Enc(1, n), allowing the ciphertext to be divided by n for an effective quotient operation in the decryption phase, thereby reducing the amount of decryption calculations.

[0130] The BHB design based on ACE-improved homomorphic searchable encryption proposed in this embodiment provides efficient additive homomorphic properties, allowing users to perform search operations in an encrypted state and verify and process the results in the decryption phase. At the same time, by combining the ACE algorithm and the BHB design, the privacy of the data can be protected and the security of the data during the search and decryption process can be ensured.

[0131] In summary, the BHB design based on homomorphic searchable encryption improves the Paillier encryption system by introducing the ACE algorithm, achieving efficient additive homomorphic properties. This improvement is of great significance in cloud data processing scenarios, providing users with secure and efficient encrypted data search and result decryption functions. At the same time, it also protects the privacy of data and ensures the security of data during transmission and storage.

[0132] S104: Decryption and verification.

[0133] After receiving the encrypted search results, the user uses the key to decrypt the results and obtain the plaintext results. At the same time, the user verifies the decrypted results to ensure the integrity and credibility of the data.

[0134] When the user receives the encrypted search results, he first needs to use the corresponding key to decrypt the results to obtain the plaintext results. In the Paillier encryption system, the user uses the private key (λ, μ) to perform the decryption operation.

[0135] During the decryption process, the user takes the encrypted search results as input and uses the private key (λ, μ) to perform calculations to obtain the plaintext results. The specific decryption calculation formula is m = (L (c^λ mod n^2) * μ mod n-1) / n, where c is the encrypted search results.

[0136] Through the decryption operation, users can obtain the plaintext content of the search results. However, the decrypted results may be affected by the data integrity and credibility. Therefore, users also need to verify the decrypted results to ensure the integrity and credibility of the data.

[0137] Data integrity verification can be achieved by using a message authentication code (MAC) or a digital signature. Users can perform MAC calculation or digital signature verification on the decrypted plaintext result to ensure that the result has not been tampered with or damaged during transmission.

[0138] Data credibility verification can be achieved by using digital certificates. The cloud platform can use digital certificates to sign search results, and users can use the corresponding public key to verify the digital signature to ensure that the results come from a trusted cloud platform.

[0139] Through decryption and verification operations, users can ensure that the plaintext results obtained are verified for integrity and authenticity. This allows users to trust the results and use them in subsequent data processing and analysis.

[0140] This decryption and verification process ensures the integrity and credibility of data while protecting data privacy. Users can effectively protect data security by using the key to decrypt the encrypted result and then verifying the integrity and credibility of the result through verification operations. The decryption and verification process is an important part of the homomorphic searchable encryption scheme based on the Paillier encryption system. Users can use the private key to decrypt the encrypted result, and then perform integrity and credibility verification to obtain a credible plaintext result. This process is of great significance in cloud data processing scenarios, providing users with secure and reliable data processing and analysis services.

[0141] By using a homomorphic searchable encryption design based on BHB, users can receive encrypted search results and decrypt them using a key to obtain plaintext results. Users can also verify the decrypted results to ensure the integrity and credibility of the data. This solution provides users with the ability to obtain credible search results while protecting data privacy, enhancing the reliability of data processing and analysis.

[0142] S105: Data processing and analysis.

[0143] Convolutional Neural Networks (CNN) or Long Short-Term Memory Networks (LSTM) are used on the decrypted plaintext data to extract valuable information. The BHB-based design provides users with the ability to decrypt encrypted data, allowing deep learning algorithms to be used to process decrypted plaintext data.

[0144] The BHB-based design provides a secure decryption function, allowing users to use deep learning algorithms to process decrypted plaintext data while protecting data privacy. This solution is of great significance in cloud data processing scenarios, as it not only meets the needs of data security, but also provides users with powerful data analysis and insights, further enhancing the value and efficiency of data processing.

[0145] In the scenario of cloud data processing, it is assumed that users perform further data processing and analysis on the decrypted plaintext data and use deep learning algorithms to extract valuable information from it. Take a virtualized cloud platform as an example, which hosts a large amount of encrypted data and provides data processing and analysis services.

[0146] Users use the BHB design to perform homomorphic searchable encryption on their data and store the encrypted data on the cloud platform. When data processing and analysis is required, users send encrypted search requests to the cloud platform, which uses the homomorphic searchable encryption algorithm to match the encrypted index and return the encrypted search results. After receiving the encrypted search results, users use the key to perform decryption operations to obtain the plaintext search results.

[0147] Taking text data as an example, users can use deep learning algorithms to further process and analyze the decrypted plaintext data. For example, users can use CNN to build a text classification model to perform sentiment analysis on the decrypted text data to determine the user's attitude and evaluation of a product or service. Users can also use LSTM to build a language model to perform machine translation on the decrypted text data, automatically generate summaries or generate new text content.

[0148] In the context of the chaos testing platform, the application of this deep learning algorithm can help users dig out deeper information from decrypted plaintext data. Users can call deep learning models on the cloud platform and use the computing resources and storage capabilities provided by the platform for efficient data processing and analysis. In this way, users can gain valuable insights from encrypted data and use this information to support business decisions, improve products and services, and enhance the effectiveness and competitiveness of the overall business while protecting data privacy.

[0149] The application of homomorphic searchable encryption combined with deep learning algorithms based on BHB design has broad application prospects in cloud data processing. By uploading encrypted data to the cloud platform and applying deep learning algorithms on the decrypted plaintext data, users can extract valuable information from it and perform efficient data processing and analysis on the chaos test platform. Such a solution not only protects the privacy and security of data, but also provides powerful data analysis capabilities, bringing users better data-driven decision-making and business innovation opportunities.

[0150] In another embodiment, the present invention proposes a cloud data processing system based on a chaos test platform, which corresponds to the cloud data processing method based on a chaos test platform proposed in the first embodiment. Figure 2 As shown, including:

[0151] Data encryption and index building module, used to perform homomorphic searchable encryption on data locally and build corresponding encrypted indexes;

[0152] Encrypted data upload and storage module, used to upload encrypted data and indexes to the cloud platform through a secure channel and store them in encrypted form;

[0153] Homomorphic search module, used to receive encrypted search requests, perform search operations in an encrypted state, and return encrypted search results;

[0154] A decryption and verification module is used to decrypt the encrypted search results using a key, obtain the plaintext data, and perform data integrity verification and data credibility verification;

[0155] The data processing and analysis module is used to extract information using convolutional neural networks or long short-term memory networks on decrypted plaintext data.

[0156] The functions and workflow of each module of this system are the same as the steps of the method in Example 1, so they will not be repeated here.

[0157] In another embodiment, the present invention proposes a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the cloud data processing method based on the chaos testing platform as described in the first embodiment.

[0158] In another embodiment, the present invention proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the cloud data processing method based on the chaos testing platform as described in Example 1 is implemented.

[0159] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0160] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0161] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A cloud data processing method based on a chaos testing platform, characterized in that: include: Perform homomorphic searchable encryption on the data locally and build the corresponding encryption index; Upload the encrypted data and index to the cloud platform through a secure channel and store them in encrypted form; The cloud platform receives the encrypted search request, performs the search operation in an encrypted state, and returns the encrypted search results; Use the key to decrypt the encrypted search results, obtain the plaintext data, and perform data integrity verification and data credibility verification; Convolutional neural networks or long short-term memory networks are used on the decrypted plaintext data to extract information.

2. A cloud data processing method based on a chaos testing platform as claimed in claim 1, characterized in that: The encrypted index is a BHB index that combines a B-tree encrypted index, a hash encrypted index, and a Bitmap encrypted index.

3. A cloud data processing method based on a chaos testing platform as claimed in claim 2, characterized in that: The BHB index uses a B-tree as an index structure. The B-tree contains multiple nodes, each of which contains one or more encrypted keywords and a pointer to a child node. The nodes of the B-tree encrypt the keywords using a homomorphic searchable encryption algorithm and are adjusted according to the insertion, deletion and balance operations of the B-tree. The BHB index converts each keyword into a unique address using a hash function, and uses the address as the keyword of the B-tree node; The hash function uses a homomorphic searchable encryption algorithm to encrypt the keyword and calculate the address of the keyword in the encrypted state; The BHB index utilizes a Bitmap index to maintain a bit vector for each possible keyword value, each bit of the bit vector corresponds to a document or data record, and the bit vector is operable in an encrypted state.

4. A cloud data processing method based on a chaos testing platform as claimed in claim 1, characterized in that: The cloud platform uses a homomorphic encryption algorithm based on the Paillier encryption system to perform calculations, matches search requests with encrypted indexes, and returns encrypted search results.

5. A cloud data processing method based on a chaos testing platform as claimed in claim 4, characterized in that: The homomorphic encryption algorithm based on the Paillier encryption system includes a key generation phase, an encryption phase and a decryption phase; In the key generation phase, two prime numbers p and q are selected, and the modulus n=pq and λ=lcm(p-1,q-1) are calculated; a random integer g is selected so that g belongs to the multiplication group of n^2; the modular inverse element μ=(L(g^λmodn∧2))∧(-1)modn of n is calculated, where L(u)=(u-1) / n; then the public key is (n, g) and the private key is (λ, μ); In the encryption phase, given the plaintext m and the public key (n, g), the ACE algorithm is used for encryption; In the decryption phase, given the ciphertext c and the private key (λ, μ), the ACE algorithm is used for decryption.

6. A cloud data processing method based on a chaos testing platform as claimed in claim 5, characterized in that: The encryption using the ACE algorithm is specifically as follows: a random number r is selected, and a ciphertext c=(g^m*r∧n mod n∧2)*Enc(1,n)^r mod n^2 is calculated, where Enc(1,n) is the result of encrypting the plaintext; The decryption using the ACE algorithm is specifically as follows: calculating the plaintext m=(L(c^λmod n∧2)*μmod n-1) / n.

7. A cloud data processing method based on a chaos testing platform as claimed in claim 6, characterized in that: The data integrity verification is achieved by using a message authentication code or a digital signature; The data authenticity verification is achieved by using a digital certificate.

8. A cloud data processing system based on a chaos testing platform, characterized in that: include: Data encryption and index building module, used to perform homomorphic searchable encryption on data locally and build corresponding encrypted indexes; Encrypted data upload and storage module, used to upload encrypted data and indexes to the cloud platform through a secure channel and store them in encrypted form; Homomorphic search module, used to receive encrypted search requests, perform search operations in an encrypted state, and return encrypted search results; A decryption and verification module is used to decrypt the encrypted search results using a key, obtain the plaintext data, and perform data integrity verification and data credibility verification; The data processing and analysis module is used to extract information using convolutional neural networks or long short-term memory networks on decrypted plaintext data.

9. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables the computer to execute the cloud data processing method based on the chaos testing platform as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the cloud data processing method based on the chaos testing platform as described in any one of claims 1 to 7 is implemented.

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