Privacy computing security system based on domestic cryptographic algorithm

Abstract

The invention provides a privacy computing security system based on a domestic cryptographic algorithm. The privacy computing security system comprises a key management module, a data encryption module, a homomorphic encryption module, a differential privacy module, an intelligent threat detection module and an access control module. The key management module generates a key by adopting an anti-quantum computing algorithm based on a learning and sampling problem LWE, and performs distributed storage and management by utilizing a block chain technology; the data encryption module performs data encryption and decryption by using an SM4 algorithm; the homomorphic encryption module executes arithmetic and logic operations in a data encryption state; the differential privacy module dynamically protects data privacy; the intelligent threat detection module monitors and detects abnormal activities in real time based on a deep learning model; and the access control module manages user access authority. According to the method, multiple security technologies are integrated, so that the anti-quantum computing capability, data privacy protection and computing efficiency are improved, and the method is suitable for data processing scenes needing high security and privacy protection.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a privacy computing security system based on a domestic cryptographic algorithm. Background Art

[0002] With the rapid development of big data and cloud computing, data privacy and security protection have become important research areas. Traditional data encryption methods, such as symmetric encryption algorithms (such as AES) and asymmetric encryption algorithms (such as RSA), play a key role in data storage and transmission security. These methods rely on the length and complexity of the key to resist cracking. However, with the advancement of quantum computing technology, traditional encryption methods face the risk of being quickly cracked. In addition, in order to meet the needs of multi-party data sharing and joint computing, homomorphic encryption and differential privacy technologies have gradually been introduced in recent years, but they still face the problems of low computational efficiency and high algorithm complexity in practical applications.

[0003] Although the above technologies can effectively protect data privacy under specific conditions, they have the following significant shortcomings: First, traditional encryption algorithms do not have the ability to resist quantum computing attacks, especially in the context of the increasing maturity of quantum computing technology, their security faces serious challenges; second, although homomorphic encryption technology can perform data calculations in an encrypted state, its complex algorithm leads to low computing efficiency and is difficult to promote in large-scale data processing scenarios; finally, existing differential privacy protection technologies usually rely on a fixed noise addition mechanism and cannot dynamically adjust the privacy protection strategy according to the actual scenario, resulting in a contradiction between the privacy protection effect and data availability. Summary of the invention

[0004] In response to the problems of lack of resistance to quantum computing attacks, low computing efficiency and inflexible privacy protection strategies, the present invention proposes a privacy computing security system based on domestic cryptographic algorithms, which uses an anti-quantum computing algorithm based on the learning and sampling problem (LWE) to generate keys, significantly improving the system's ability to resist quantum computing attacks; secondly, by introducing parallel computing technology to optimize the homomorphic encryption module, the efficiency of data calculation in the encrypted state is improved, and the problem of high computational complexity of existing homomorphic encryption technology is solved; finally, the present invention uses a differential privacy protection module with a hash algorithm and an adaptive noise addition mechanism, which can dynamically adjust the privacy protection strategy, improves the flexibility and effectiveness of data privacy protection, and takes into account the availability and security of data.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0006] A privacy computing security system based on a domestic cryptographic algorithm, the system comprising:

[0007] Key management module: used to generate, distribute, update and destroy keys. It uses a quantum computing-resistant algorithm based on the learning and sampling problem (LWE) to generate keys, and uses blockchain technology for distributed storage and management of keys to prevent tampering and improve key security.

[0008] Data encryption module: Use SM4 encryption algorithm to encrypt and decrypt data;

[0009] Homomorphic encryption module: including arithmetic operation unit, logic operation unit and batch processing unit, which can perform arithmetic and logic operations in an encrypted state and improve operation efficiency through parallel computing technology;

[0010] Differential privacy module: uses hashing algorithm and random noise adding mechanism to protect user data privacy during data processing and analysis;

[0011] Intelligent Threat Detection Module: uses a deep learning-based behavioral analysis model to monitor and detect abnormal activities in the system;

[0012] Access control module: Combines biometric information and dynamic key mechanism to control and manage user access rights.

[0013] Preferably, the key management module includes:

[0014] Key generation unit: uses quantum computing-resistant LWE algorithm to generate initial keys, and verifies key validity and integrity through digital signature technology;

[0015] Key distribution unit: Use the blockchain network to achieve decentralized distributed storage of keys, and use consensus algorithms PoW or PoS to ensure data consistency and security.

[0016] Preferably, the key generation algorithm based on the learning and sampling problem LWE dynamically adjusts the dimensions d and k of the public key matrix A according to the computing environment and security requirements through an adaptive parameter adjustment mechanism to optimize the balance between computational complexity and security. The specific calculation formula is as follows:

[0017] The key generation algorithm based on the learning and sampling problem LWE uses an adaptive parameter adjustment mechanism to dynamically adjust the dimensions d and k of the public key matrix A according to the computing environment and security requirements, thereby optimizing the balance between computational complexity and security. The calculation formula is as follows:

[0018]

[0019] b=A*s+e(mod q)

[0020] Where: A is the public key matrix, randomly generated, and takes values ​​in the finite field Z q; q is the modulus, which is a prime number, usually large, and is used to limit the range of matrix and vector elements; d and k are adaptive adjustment parameters, representing the number of rows and columns of the matrix, respectively, and are dynamically adjusted according to system requirements; s is the secret vector, which is one of the private key components; e is the error vector, whose elements are small random integers, which enhances the system's anti-attack capability; b is the generated key component, which is output as the public key;

[0021] The key generation unit includes an adaptive security assessment mechanism, which dynamically adjusts the generation parameters by calculating the security score of the key to ensure that the generated key meets the expected security standards. The calculation formula is as follows:

[0022]

[0023] Where: K is the generated key; SecurityScore(K) is the security score of the key, which is used to evaluate the security of the key; Metric i (K) is a security metric related to the key K, including key entropy and randomness; w i is the weight of each security metric, which is used to adjust the importance of each metric in the overall score;

[0024] During the key generation process, if a potential error or security vulnerability is detected, the system will automatically trigger the online error correction mechanism to correct the generated key without regenerating the entire key. The calculation formula is as follows:

[0025] K′=Correct(K,Errors)

[0026] Where: K is the originally generated key; Errors are potential errors and vulnerabilities detected during the key generation process; Correct(·) is the algorithm used to correct the key, which adjusts K according to the list of detected errors; K′ is the corrected key, ensuring that potential security risks are eliminated.

[0027] Preferably, the key management module is configured to generate and distribute keys only in compliance with a predetermined authorization policy, wherein the authorization policy includes:

[0028] Multi-factor authentication (MFA) mechanism verifies user identity through a combination of user password, dynamic password and biometric information;

[0029] Dynamic permission control, dynamically adjusting user permissions based on the user's security level and access history;

[0030] Operation log audit: real-time recording and analysis of key generation and distribution operation logs.

[0031] Preferably, the data encryption module further includes:

[0032] The data decryption unit uses the SM4 decryption algorithm to perform decryption operations after receiving the encrypted data.

[0033] Preferably, the homomorphic encryption module includes:

[0034] An arithmetic unit for performing homomorphically encrypted addition and multiplication operations on encrypted data, where:

[0035] The addition operation is implemented by the homomorphic addition operation formula:

[0036] C add =C 1 +C 2 (mod q)

[0037] Among them, C 1 and C 2 are two sets of ciphertext data, (mod q) represents the operation modulo q, q is a predetermined modulus, usually a large prime number;

[0038] The multiplication operation is performed by homomorphic multiplication operation C mult Formula implementation:

[0039] C mult =C 1 *C 2 (mod q)

[0040] A logic operation unit, used to perform logic operations on encrypted data, including AND, OR, and NOT operations;

[0041] Batch optimization unit, used to batch process multiple data blocks to improve computing efficiency, where:

[0042] The batch addition formula is:

[0043] BatchAdd(C batch1 ,C batch2 )=C batch1 +C batch2 (mod q)

[0044] Among them, C batch1 and C batch2 Batch encrypt data for two groups, using polynomial encoding to pack multiple data together;

[0045] The batch multiplication formula is:

[0046] BatchAdd(C batch1 ,C batch2 )=C batch1 *C batch2 (mod q)

[0047] Parallel computing unit, used to improve the computing performance of homomorphic encryption operations through multithreading, where:

[0048] The formula for parallel computing is:

[0049]

[0050] Among them, Compute (C i ) indicates that the ciphertext operations are executed in parallel in a multi-threaded or multi-processor environment. Represents the aggregation operation of multiple ciphertext data, which accelerates the calculation through parallel processing.

[0051] Preferably, the access control module includes:

[0052] Biometric unit: collects and identifies the user's fingerprint, iris or facial features;

[0053] Permission verification unit: Combines the user's biometric information with a dynamically generated key to verify the user's access rights.

[0054] Preferably, the intelligent threat detection module includes:

[0055] Behavior analysis unit: collects real-time data streams from the system and uses deep learning-based machine learning models to analyze behavioral features to identify potential abnormal behaviors;

[0056] Anomaly Detection Unit: Generates alerts when behavior is detected that deviates from normal patterns and automatically executes pre-set security response actions, including isolating affected system components.

[0057] Preferably, the system further comprises:

[0058] Audit module: records and stores logs of all access and operations, uses hash chain technology to generate tamper-proof log files, and ensures the integrity and authenticity of log data.

[0059] Preferably, when the system detects unauthorized access or operation, it will automatically trigger multiple layers of security response measures, including:

[0060] Send real-time alert notifications to system administrators;

[0061] Perform account lockout or session termination operations;

[0062] Enable the machine learning-based adaptive security policy adjustment module to dynamically adjust the system's defense measures according to the current threat level.

[0063] Terminology explanation:

[0064] Quantum-resistant algorithm (LWE algorithm): The quantum-resistant algorithm based on the learning and sampling problem (LWE) is an encryption algorithm for generating keys that can maintain high security in the face of quantum computer attacks. The LWE algorithm uses the difficult problem of adding small random errors to a modular linear equation system in a high-dimensional vector space, and has been proven to have strong anti-cracking capabilities in a quantum computing environment. In the present invention, the LWE algorithm is used to generate the initial key, and through an adaptive parameter adjustment mechanism, the parameters of the public key matrix (such as dimensions d and k) are dynamically adjusted according to the computing environment and security requirements to achieve the best balance between computational complexity and security.

[0065] Blockchain technology: Blockchain technology is a distributed ledger technology that ensures data consistency and immutability through consensus algorithms (such as proof of work PoW or proof of stake PoS). In the present invention, blockchain technology is used for distributed storage and management of keys to prevent keys from being tampered with and unauthorized access. The decentralized storage mechanism of blockchain enhances the security of keys, and the automation of key distribution and management can be achieved through smart contracts.

[0066] Homomorphic encryption: Homomorphic encryption is an encryption technology that allows arithmetic and logical operations to be performed directly on encrypted data. Common homomorphic encryption includes additive homomorphism and multiplicative homomorphism, which allow addition and multiplication operations to be performed on ciphertext respectively without decrypting the data. In the present invention, the homomorphic encryption module can perform arithmetic and logical operations while the data remains encrypted, and improves the operation efficiency through parallel computing technology. This method ensures that the data is not leaked during transmission and calculation, and is suitable for high-security data processing scenarios.

[0067] Deep learning model: A deep learning model refers to a machine learning model that uses a deep neural network structure to analyze and process complex data. In the present invention, a behavior analysis model based on deep learning is used to monitor the operating status of the system and user behavior in real time, and to identify abnormal activities and potential threats in the system through trained neural networks (such as convolutional neural networks (CNN) or recurrent neural networks (RNN). The model can continuously learn and adjust to improve the accuracy and response speed of threat detection.

[0068] Adaptive noise addition mechanism: The adaptive noise addition mechanism is a differential privacy protection method that protects user privacy by injecting random noise into the data. The mechanism can dynamically adjust the intensity and distribution of noise according to the type of data, usage scenario, and user needs, ensuring that privacy can be effectively protected while maintaining the practicality of data during data analysis. The hash algorithm is used for fast data processing and retrieval, and the addition of random noise enhances the privacy protection effect.

[0069] Multi-factor authentication (MFA) mechanism: Multi-factor authentication (MFA) mechanism is an identity authentication method that verifies the identity of a user by combining different types of authentication factors (such as user passwords, dynamic passwords, and biometric information). In the present invention, the MFA mechanism combines user passwords, dynamic passwords, and biometric information (such as fingerprints, irises, etc.) to improve the security of identity authentication and ensure that only authorized users can access systems and data.

[0070] Dynamic permission control: Dynamic permission control is a real-time permission adjustment mechanism based on user behavior, security level and access history. It dynamically adjusts user access rights based on user behavior patterns, access history, system policies and other factors to ensure that only compliant operations and behaviors can pass authentication, thereby improving the security and flexibility of the system.

[0071] Intelligent Threat Detection Module: The Intelligent Threat Detection Module monitors abnormal activities in the system in real time through the "Behavior Analysis Unit" and "Anomaly Detection Unit". The Behavior Analysis Unit uses deep learning models to analyze the system's real-time data stream to identify potential abnormal behaviors. When the Anomaly Detection Unit detects that the behavior deviates from the normal pattern, it generates an alarm and automatically executes the preset security response measures, such as isolating the affected system components or triggering security protection mechanisms.

[0072] Hash chain technology: Hash chain technology is a technology that links multiple data items together through a hash algorithm to ensure the integrity and immutability of data. In the audit module of the present invention, hash chain technology is used to generate tamper-proof log files to ensure the authenticity and integrity of all operations and access records, providing technical support for the audit and compliance inspection of the system.

[0073] Online error correction mechanism: Online error correction mechanism is a method to detect and correct potential errors or security vulnerabilities during key generation and use. This mechanism uses an automated algorithm to correct the key based on the detected error list without regenerating the entire key, thereby improving the efficiency and security of key management.

[0074] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention adopts a quantum computing-resistant algorithm based on the learning and sampling problem (LWE) to generate a key, which has a stronger ability to resist quantum computing attacks than traditional encryption algorithms. By using this quantum computing-resistant cryptographic algorithm, the present invention can significantly improve the security of the key and the overall protection capability of the system in the face of quantum computing threats, and ensure the high security of data during transmission and storage. Secondly, the present invention introduces parallel computing technology in the homomorphic encryption module, and uses multi-threading and multi-processor architecture to optimize data computing operations in the encrypted state. This improvement significantly improves the computational efficiency of homomorphic encryption, enabling it to achieve fast and efficient calculations in large-scale data processing scenarios, thereby solving the problem of high computational complexity and low efficiency of existing homomorphic encryption technology. In addition, the differential privacy module of the present invention adopts a hash algorithm and an adaptive noise addition mechanism, which can dynamically adjust the privacy protection strategy according to the data type, usage scenario and user needs. This flexibility enables the system to maintain the availability of data while providing a high level of privacy protection, solving the contradiction between privacy protection effect and data availability in existing differential privacy technologies. The present invention also integrates an intelligent threat detection module, which uses a behavior analysis model based on deep learning to monitor and detect abnormal activities in the system in real time. Through the behavior analysis and anomaly detection unit, the present invention can quickly identify potential security threats and automatically execute preset security response operations, such as isolating affected system components, thereby providing multi-level security protection and further improving the overall security and stability of the system. In terms of key management and access control, the key management module of the present invention uses blockchain technology to realize the distributed storage and management of keys, and combines the multi-factor authentication (MFA) mechanism and dynamic permission control strategy to achieve secure management and flexible allocation of keys. At the same time, the access control module combined with biometric information and dynamic key mechanism ensures the authenticity of user identity and the security of system access, and effectively prevents unauthorized access and operation. Finally, the present invention also includes an audit module that uses hash chain technology to generate an unalterable log file to record and store logs of all accesses and operations. This mechanism ensures the integrity and authenticity of log data, provides strong technical support for post-audit and compliance inspection, and enhances the transparency and credibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0076] in:

[0077] Figure 1 A schematic diagram of the system architecture of an embodiment of the present invention;

[0078] Figure 2 This is a structural diagram of a key management module in an embodiment of the present invention;

[0079] Figure 3 : is a workflow diagram of a homomorphic encryption module in an embodiment of the present invention;

[0080] Figure 4 4 is a detection flow chart of the intelligent threat detection module in an embodiment of the present invention. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0082] Example 1

[0083] like Figure 1 , Figure 2 As shown, it is an embodiment of the present invention, which provides a privacy computing security system based on a domestic cryptographic algorithm, including:

[0084] Key management module: used to generate, distribute, update and destroy keys. It uses a quantum computing-resistant algorithm based on the learning and sampling problem (LWE) to generate keys, and uses blockchain technology for distributed storage and management of keys to prevent tampering and improve key security.

[0085] The key management module is used to generate, distribute, update and destroy keys to ensure the security of all keys used in the system throughout their life cycle. This module uses a quantum computing-resistant algorithm based on the learning and sampling problem (LWE) to generate keys. The LWE algorithm is an encryption algorithm that is resistant to quantum attacks and can maintain high security in the face of attacks from quantum computers. This module uses an adaptive parameter adjustment mechanism to dynamically adjust the parameters of the public key matrix according to the computing environment and security requirements to optimize the balance between computational complexity and security.

[0086] The key management module also uses blockchain technology for distributed storage and management of keys. The tamper-proof nature of blockchain ensures the integrity and security of keys during storage and transmission. In addition, the key management module supports a multi-factor authentication mechanism that verifies user identity by combining user passwords, dynamic passwords, and biometric information to ensure that only authorized users can generate and access keys.

[0087] Furthermore, the key management module includes:

[0088] Key generation unit: uses quantum computing-resistant LWE algorithm to generate initial keys, and verifies key validity and integrity through digital signature technology;

[0089] Key distribution unit: Use the blockchain network to achieve decentralized distributed storage of keys, and use consensus algorithms PoW or PoS to ensure data consistency and security.

[0090] Specifically, the key generation algorithm based on the learning and sampling problem LWE dynamically adjusts the dimensions d and k of the public key matrix A according to the computing environment and security requirements through an adaptive parameter adjustment mechanism to optimize the balance between computational complexity and security. The specific calculation formula is as follows:

[0091] The key generation algorithm based on the learning and sampling problem LWE uses an adaptive parameter adjustment mechanism to dynamically adjust the dimensions d and k of the public key matrix A according to the computing environment and security requirements, thereby optimizing the balance between computational complexity and security. The calculation formula is as follows:

[0092]

[0093] b=A*s+e(mod q)

[0094] Where: A is the public key matrix, randomly generated, and takes values ​​in the finite field Z q ; q is the modulus, which is a prime number, usually large, and is used to limit the range of matrix and vector elements; d and k are adaptive adjustment parameters, representing the number of rows and columns of the matrix, respectively, and are dynamically adjusted according to system requirements; s is the secret vector, which is one of the private key components; e is the error vector, whose elements are small random integers, which enhances the system's anti-attack capability; b is the generated key component, which is output as the public key;

[0095] The key generation unit includes an adaptive security assessment mechanism, which dynamically adjusts the generation parameters by calculating the security score of the key to ensure that the generated key meets the expected security standards. The calculation formula is as follows:

[0096]

[0097] Where: K is the generated key; SecurityScore(K) is the security score of the key, which is used to evaluate the security of the key; Metric i (K) is a security metric related to the key K, including key entropy and randomness; w i is the weight of each security metric, which is used to adjust the importance of each metric in the overall score;

[0098] During the key generation process, if a potential error or security vulnerability is detected, the system will automatically trigger the online error correction mechanism to correct the generated key without regenerating the entire key. The calculation formula is as follows:

[0099] K′=Correct(K,Errors)

[0100] Where: K is the originally generated key; Errors are potential errors and vulnerabilities detected during the key generation process; Correct(·) is the algorithm used to correct the key, which adjusts K according to the list of detected errors; K′ is the corrected key, ensuring that potential security risks are eliminated.

[0101] The key management module is configured to generate and distribute keys only in compliance with a predetermined authorization policy, wherein the authorization policy includes:

[0102] Multi-factor authentication (MFA) mechanism verifies user identity through a combination of user password, dynamic password and biometric information;

[0103] Dynamic permission control, dynamically adjusting user permissions based on the user's security level and access history;

[0104] Operation log audit: real-time recording and analysis of key generation and distribution operation logs.

[0105] Further, the key management module is configured to generate and distribute keys only in compliance with a predetermined authorization policy, wherein the authorization policy includes:

[0106] Multi-factor authentication (MFA) mechanism verifies user identity through a combination of user password, dynamic password and biometric information;

[0107] Dynamic permission control, dynamically adjusting user permissions based on the user's security level and access history;

[0108] Operation log audit: real-time recording and analysis of key generation and distribution operation logs.

[0109] Furthermore, the data encryption module: uses the SM4 encryption algorithm to perform encryption and decryption operations on the data;

[0110] Specifically, the data encryption module also includes:

[0111] The data decryption unit uses the SM4 decryption algorithm to perform decryption operations after receiving the encrypted data.

[0112] The data encryption module is responsible for encrypting and decrypting data transmitted and stored in the system. This module uses the SM4 encryption algorithm, which is a symmetric encryption algorithm that complies with China's national cryptographic standards and has efficient encryption and decryption speed and strong security. The data encryption module ensures that even if the data is intercepted or leaked during data transmission or storage, it cannot be interpreted by unauthorized persons. This module can also work with other modules to protect the security of data during homomorphic encryption calculations and differential privacy protection.

[0113] Further, such as Figure 3 As shown, the homomorphic encryption module includes an arithmetic operation unit, a logic operation unit and a batch processing unit, which can perform arithmetic and logic operations in an encrypted state and improve the operation efficiency through parallel computing technology;

[0114] Specifically, the homomorphic encryption module includes:

[0115] An arithmetic unit for performing homomorphically encrypted addition and multiplication operations on encrypted data, where:

[0116] The addition operation is implemented by the homomorphic addition operation formula:

[0117] C add =C 1 +C 2 (mod q)

[0118] Among them, C 1 and C 2 are two sets of ciphertext data, (mod q) represents the operation modulo q, q is a predetermined modulus, usually a large prime number;

[0119] The multiplication operation is performed by homomorphic multiplication operation C mu lt formula implementation:

[0120] C mult =C 1 *C 2 (mod q)

[0121] A logic operation unit, used to perform logic operations on encrypted data, including AND, OR, and NOT operations;

[0122] Batch optimization unit, used to batch process multiple data blocks to improve computing efficiency, where:

[0123] The batch addition formula is:

[0124] BatchAdd(C batch1 ,C batch2 )=C batch1 +C batch2 (mod q)

[0125] Among them, C batch1 and C batch2 Batch encrypt data for two groups, using polynomial encoding to pack multiple data together;

[0126] The batch multiplication formula is:

[0127] BatchAdd(C batch1 ,C batch2 )=C batch1 *C batch2 (mod q)

[0128] Parallel computing unit, used to improve the computing performance of homomorphic encryption operations through multithreading, where:

[0129] The formula for parallel computing is:

[0130]

[0131] Among them, Compute (C i ) indicates that the ciphertext operations are executed in parallel in a multi-threaded or multi-processor environment. Represents the aggregation operation of multiple ciphertext data, which accelerates the calculation through parallel processing.

[0132] The homomorphic encryption module includes an arithmetic operation unit, a logic operation unit, and a batch processing unit, which can directly perform arithmetic and logic operations while the data remains encrypted. The arithmetic operation unit is used to perform addition and multiplication operations on encrypted data, while the logic operation unit is used to perform logical operations such as AND, OR, and NOT.

[0133] This module can significantly speed up large-scale data processing by using parallel computing technology to improve computing efficiency. The batch unit can process multiple data blocks in parallel, further optimizing the performance of homomorphic encryption operations. This design can perform complex calculations and analyses on data without decrypting the data, ensuring the privacy and security of the data, and is particularly suitable for data analysis scenarios that require the protection of sensitive information.

[0134] Furthermore, the differential privacy module: uses hashing algorithms and random noise addition mechanisms to protect user data privacy during data processing and analysis;

[0135] The differential privacy module uses a hash algorithm and a random noise addition mechanism to protect the privacy of user data during data processing and analysis. The module dynamically adjusts the noise addition strategy based on the data type, processing requirements, and privacy protection level to ensure that no individual's privacy information is leaked during data analysis.

[0136] The differential privacy module injects random noise into the data to blur individual data, protecting privacy while ensuring the accuracy and availability of statistical analysis results. This method is particularly suitable for scenarios that require extracting statistical features from large amounts of data, machine learning training, and big data analysis, effectively preventing data leakage and privacy infringement.

[0137] Further, such as Figure 4 As shown, the intelligent threat detection module: uses a deep learning-based behavioral analysis model to monitor and detect abnormal activities in the system;

[0138] Specifically, the intelligent threat detection module includes:

[0139] Behavior analysis unit: collects real-time data streams from the system and uses deep learning-based machine learning models to analyze behavioral features to identify potential abnormal behaviors;

[0140] Anomaly Detection Unit: Generates alerts when behavior is detected that deviates from normal patterns and automatically executes pre-set security response actions, including isolating affected system components.

[0141] The intelligent threat detection module uses a deep learning-based behavioral analysis model to monitor and detect abnormal activities in the system in real time. The module contains a behavioral analysis unit and an anomaly detection unit. The behavioral analysis unit collects the real-time data stream of the system and uses deep learning algorithms to analyze the data and identify possible abnormal behaviors.

[0142] When the anomaly detection unit detects abnormal behavior, it generates an alarm and executes a preset security response action, such as isolating the affected system components or triggering a security protection mechanism. The design of this module ensures that the system can quickly identify and respond to potential security threats, enhancing the security and stability of the system.

[0143] Access control module: Combines biometric information and dynamic key mechanism to control and manage user access rights.

[0144] Furthermore, the access control module includes:

[0145] Biometric unit: collects and identifies the user's fingerprint, iris or facial features;

[0146] Permission verification unit: Combines the user's biometric information with a dynamically generated key to verify the user's access rights.

[0147] The access control module combines biometric information (such as fingerprints, irises, facial features, etc.) and a dynamic key mechanism to control and manage user access rights. The module identifies and authenticates the user's biometrics and combines it with a dynamic key generation mechanism to ensure that only authorized users can access system resources or data.

[0148] The module also supports dynamic adjustment of user permissions, adjusting access permissions in real time based on user behavior, access history, and changes in security policies, thereby further improving system security. This multi-level, multi-factor authentication and access control mechanism effectively prevents unauthorized access and potential security threats.

[0149] Furthermore, the system also includes:

[0150] Audit module: records and stores logs of all access and operations, uses hash chain technology to generate tamper-proof log files, and ensures the integrity and authenticity of log data.

[0151] Furthermore, when the system detects unauthorized access or operation, it will automatically trigger multiple layers of security response measures, including:

[0152] Send real-time alert notifications to system administrators;

[0153] Perform account lockout or session termination operations;

[0154] Enable the machine learning-based adaptive security policy adjustment module to dynamically adjust the system's defense measures according to the current threat level.

[0155] Example 2

[0156] In this embodiment, an improved key management process is introduced to enhance the security of key generation, storage and distribution. The key management module generates the initial key using a quantum computing-resistant algorithm based on the learning and sampling problem (LWE). During the key generation process, the key generation unit achieves the best balance between computational complexity and security by dynamically adjusting the dimensions (d and k) of the public key matrix A. The public key matrix A is randomly generated and takes values ​​from a finite field Zq, where q is a large prime number used to limit the range of matrix and vector elements. The secret vector s is used as one of the private key components, and is combined with the error vector e (small random integer) to further enhance the system's anti-attack capability.

[0157] To ensure the security and integrity of the keys, the key generation unit also includes an adaptive security assessment mechanism. This mechanism calculates the security score of the key and dynamically adjusts the generation parameters to ensure that the generated key meets the expected security standards. If a potential error or security vulnerability is detected, the system automatically triggers the online error correction mechanism to correct the generated key without regenerating the entire key. This approach not only improves the efficiency of the key generation process, but also enhances the overall security of the system.

[0158] In addition, the key distribution unit uses the blockchain network to achieve decentralized distributed storage of keys. Consensus algorithms such as proof of work (PoW) or proof of stake (PoS) are used to ensure data consistency and security, thereby preventing tampering and unauthorized access. This distributed storage method combines multi-factor authentication (MFA) mechanisms, such as user passwords, dynamic passwords, and biometric information (such as fingerprints, irises, etc.) to verify user identities. The module also includes dynamic permission control and operation log auditing functions to monitor and analyze operations in the key generation and distribution process in real time, further improving the security and reliability of key management.

[0159] Example 3

[0160] This embodiment describes in detail the specific implementation of the homomorphic encryption module. In this module, the system performs addition, multiplication and logical operations (such as AND, OR, NOT) on the encrypted data through the arithmetic operation unit and the logic operation unit. The arithmetic operation unit can perform addition and multiplication operations of homomorphic encryption in the encrypted state, and its addition operation is implemented by the homomorphic addition operation formula, in which the ciphertext data C1 and C2 are operated under the modulus q; the multiplication operation is implemented by the homomorphic multiplication operation formula to ensure secure calculation in the encrypted data state.

[0161] In order to improve data processing efficiency, the module also includes a batch optimization unit that uses polynomial encoding to package multiple data blocks together for simultaneous batch processing. Batch addition and multiplication formulas further optimize computational efficiency, resulting in significant performance improvements in large-scale data processing. In addition, through the parallel computing unit, the module uses multi-threading and multi-processor environments to achieve parallel processing of ciphertext data, accelerate the calculation of homomorphic encryption operations, and enhance the real-time and response speed of data calculations.

[0162] Example 4

[0163] This embodiment illustrates the specific application of the differential privacy module. During data processing and analysis, in order to protect user data privacy, the differential privacy module uses a hash algorithm and a random noise addition mechanism. The module can dynamically adjust the noise addition strategy according to the data type and usage scenario to maintain data availability while protecting data privacy. This flexible differential privacy strategy can adapt to various complex data analysis scenarios to ensure the best balance between data privacy protection and actual application effects.

[0164] In summary, the present invention has comprehensively improved data security and privacy protection capabilities by integrating multiple security mechanisms and technologies. Specifically, the present invention uses a quantum computing algorithm based on the learning and sampling problem (LWE) to generate keys, which significantly improves the system's protection capabilities under quantum computing attacks; by introducing parallel computing technology to optimize the homomorphic encryption module, the efficiency of data calculation in the encrypted state is improved, and the problems of high computational complexity and low efficiency of existing homomorphic encryption technology are solved; at the same time, the present invention uses a differential privacy protection module with an adaptive noise addition mechanism, which can dynamically adjust the privacy protection strategy, while protecting the privacy of user data and maintaining the availability of data.

[0165] In addition, the present invention also integrates an intelligent threat detection module based on deep learning. Through behavioral analysis and anomaly detection functions, it can monitor abnormal activities in the system in real time, and automatically execute preset security response measures to provide multi-level security protection. At the same time, the system implements secure management of keys and user access through multi-factor authentication (MFA) mechanism and dynamic permission control strategy, and uses blockchain technology to ensure the distributed security of key management and storage. Finally, the audit module of the present invention uses hash chain technology to generate tamper-proof log files, which provides strong support for the compliance and security of the system.

[0166] The present invention has significant advantages in anti-quantum attack, homomorphic encryption computing efficiency, privacy protection flexibility and multi-level security protection, and can be widely used in data processing scenarios that require high security and privacy protection. Therefore, the embodiments and variants of the present invention are not limited thereto, and all modifications, equivalent substitutions and improvements under the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0167] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0168] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0169] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A privacy computing security system based on domestic cryptographic algorithms, It is characterized in that The system comprises: Key management module: used to generate, distribute, update and destroy keys. It uses a quantum computing-resistant algorithm based on the learning and sampling problem (LWE) to generate keys, and uses blockchain technology for distributed storage and management of keys. Data encryption module: Use SM4 encryption algorithm to encrypt and decrypt data; Homomorphic encryption module: including arithmetic operation unit, logic operation unit and batch processing unit, which can perform arithmetic and logic operations in an encrypted state and improve operation efficiency through parallel computing technology; Differential privacy module: uses hashing algorithm and random noise adding mechanism to protect user data privacy during data processing and analysis; Intelligent Threat Detection Module: uses a deep learning-based behavioral analysis model to monitor and detect abnormal activities in the system; Access control module: Combines biometric information and dynamic key mechanism to control and manage user access rights.

2. According to the privacy computing security system based on the domestic cryptographic algorithm according to claim 1, It is characterized in that The key management module comprises: Key generation unit: uses quantum computing-resistant LWE algorithm to generate initial keys, and verifies the key validity and integrity through digital signature technology; Key distribution unit: Use the blockchain network to achieve decentralized distributed storage of keys, and adopt a consensus algorithm to ensure data consistency and security.

3. According to the privacy computing security system based on the domestic cryptographic algorithm according to claim 1, It is characterized in that The key generation algorithm based on the learning and sampling problem LWE dynamically adjusts the dimensions d and k of the public key matrix A according to the computing environment and security requirements through an adaptive parameter adjustment mechanism. The specific calculation formula is as follows: The key generation algorithm based on the learning and sampling problem LWE uses an adaptive parameter adjustment mechanism to dynamically adjust the dimensions d and k of the public key matrix A according to the computing environment and security requirements. The calculation formula is as follows: b=A*s+e(mod q) Where: A is the public key matrix, randomly generated, and takes values ​​in the finite field Z q ; q is the modulus, which is a prime number, usually large, and is used to limit the range of matrix and vector elements; d and k are adaptive adjustment parameters, representing the number of rows and columns of the matrix respectively; s is the secret vector, which is one of the private key components; e is the error vector, whose elements are small random integers, which enhances the system's anti-attack capability; b is the generated key component, which is output as the public key; The key generation unit includes an adaptive security assessment mechanism, which dynamically adjusts the generation parameters by calculating the security score of the key to ensure that the generated key meets the expected security standards. The calculation formula is as follows: Where: K is the generated key; SecurityScore(K) is the security score of the key, which is used to evaluate the security of the key; Metric i (K) is a security metric related to the key K, including key entropy and randomness; w i is the weight of each security metric, which is used to adjust the importance of each metric in the overall score; During the key generation process, if a potential error or security vulnerability is detected, the system will automatically trigger the online error correction mechanism to correct the generated key without regenerating the entire key. The calculation formula is as follows: K′=Correct(K,Errors) Where: K is the originally generated key; Errors are potential errors and vulnerabilities detected during the key generation process; Correct(·) is the algorithm used to correct the key, which adjusts K according to the list of detected errors; K′ is the corrected key, ensuring that potential security risks are eliminated.

4. According to the privacy computing security system based on the domestic cryptographic algorithm according to claim 1, It is characterized in that The key management module is configured to generate and distribute keys only in compliance with a predetermined authorization policy, wherein the authorization policy includes: Multi-factor authentication (MFA) mechanism verifies user identity through a combination of user password, dynamic password and biometric information; dynamic permission control dynamically adjusts user permissions based on the user's security level and access history; Operation log audit: real-time recording and analysis of key generation and distribution operation logs.

5. According to the privacy computing security system based on the domestic cryptographic algorithm according to claim 1, It is characterized in that The data encryption module also includes: The data decryption unit uses the SM4 decryption algorithm to perform decryption operations after receiving the encrypted data.

6. According to claim 1, the privacy computing security system based on the domestic cryptographic algorithm, It is characterized in that The homomorphic encryption module includes: An arithmetic unit for performing homomorphically encrypted addition and multiplication operations on encrypted data, where: The addition operation is implemented by the homomorphic addition operation formula: C add =C 1 +C 2 (mod q) Among them, C 1 and C 2 are two sets of ciphertext data, (mod q) represents the operation of modulus q, q is a predetermined modulus, usually a large prime number; the multiplication operation is performed by homomorphic multiplication operation C mult Formula implementation: C mult =C 1 *C 2 (mod q) A logic operation unit, used to perform logic operations on encrypted data, including AND, OR, and NOT operations; Batch optimization unit, used to batch process multiple data blocks to improve computing efficiency, where: The batch addition formula is: BatchAdd(C batch1 ,C batch2 )=C batch1 +C batch2 (mod q) Among them, C batch1 and C batch2 Batch encrypt data for two groups, using polynomial encoding to pack multiple data together; The batch multiplication formula is: BatchAdd(C batch1 ,C batch2 )=C batch1 *C batch2 (mod q) Parallel computing unit, used to improve the computing performance of homomorphic encryption operations through multithreading, where: The formula for parallel computing is: Among them, Compute (C i ) indicates that the ciphertext operations are executed in parallel in a multi-threaded or multi-processor environment. Represents the aggregation operation of multiple ciphertext data, which accelerates the calculation through parallel processing.

7. According to claim 1, the privacy computing security system based on the domestic cryptographic algorithm, It is characterized in that The access control module comprises: Biometric unit: collects and identifies the user's fingerprint, iris or facial features; Permission verification unit: Combines the user's biometric information with a dynamically generated key to verify the user's access rights.

8. According to claim 1, the privacy computing security system based on the domestic cryptographic algorithm, It is characterized in that The intelligent threat detection module includes: Behavior analysis unit: collects real-time data streams from the system and uses deep learning-based machine learning models to analyze behavioral features to identify potential abnormal behaviors; Anomaly Detection Unit: Generates alerts when behavior is detected that deviates from normal patterns and automatically executes pre-set security response actions, including isolating affected system components.

9. According to claim 1, the privacy computing security system based on the domestic cryptographic algorithm, It is characterized in that The system also includes: an audit module: recording and storing logs of all accesses and operations, using hash chain technology to generate tamper-proof log files to ensure the integrity and authenticity of log data.

10. According to claim 1, the privacy computing security system based on the domestic cryptographic algorithm, It is characterized in that When the system detects unauthorized access or operation, multi-layered security response measures are automatically triggered, including: Send real-time alert notifications to system administrators; Perform account lockout or session termination operations; Enable the machine learning-based adaptive security policy adjustment module to dynamically adjust the system's defense measures according to the current threat level.

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