User identity data encryption and decryption method based on big data

Abstract

The application relates to the technical field of big data security, and discloses a user identity data encryption and decryption method and system based on big data, a computer device and a storage medium. The method comprises the following steps: acquiring an identity fingerprint; blind generation of a blind identity identifier and a bound dynamic confusion factor; generation of an encryption key based on the blind identity identifier, encryption of user identity data to obtain identity ciphertext; cutting of the ciphertext into ciphertext fragments, association storage of the ciphertext fragments with derived fragments of the dynamic confusion factor in different storage nodes, and inability of the derived fragments stored in a single node to reconstruct the dynamic confusion factor; acquisition of multiple ciphertext fragments and associated derived fragments in response to a request and after verification; reconstruction of the dynamic confusion factor, recombination of the identity ciphertext after verification of the integrity of the ciphertext fragments; acquisition of a corresponding decryption key based on a permission level, decryption of the identity ciphertext, and recovery of user identity data. The application cuts off the association between the fragment data and the user identity, guarantees the integrity of the fragments, and realizes field-level fine-grained access control.

Description

Technical Field

[0001] This application relates to the field of big data security technology, specifically a method, system, computer equipment, and storage medium for encrypting and decrypting user identity data based on big data. Background Technology

[0002] With the widespread application of big data technology in finance, government affairs, and other fields, the collection, storage, and sharing of user identity data have become commonplace. Ensuring the security and privacy of this sensitive data has become a critical issue that urgently needs to be addressed. To reduce the risk of data leakage from a single storage node, existing technologies typically employ data sharding, dividing the encrypted user identity data into multiple shards and distributing them across different nodes. However, this sharding approach carries the risk of data association: while attackers cannot directly obtain the complete plaintext from a single shard, they can analyze the shard access patterns across multiple nodes to gradually establish a connection between the shards and the user's identity, thereby deducing the user's identity information. Furthermore, existing solutions lack effective shard integrity verification mechanisms after sharding, failing to detect whether shards have been tampered with or replaced. Therefore, how to sever the association between shards and user identities while implementing distributed storage, and ensuring the integrity and trustworthiness of each shard's source, has become a key technical challenge in the field of user identity data protection. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, system, computer equipment, and storage medium for encrypting and decrypting user identity data based on big data, which can sever the correlation between fragments of user identity data, ensure the integrity of data after fragment storage, and realize fine-grained access control, in order to address the above-mentioned technical problems.

[0004] Firstly, a method for encrypting and decrypting user identity data based on big data is provided, the method comprising: Obtain user identity fingerprint; The identity fingerprint is blinded to generate a blinded identity identifier, and a dynamic obfuscation factor bound to the blinded identity identifier is generated simultaneously. An encryption key is generated based on the blinded identity identifier, and the user identity data is encrypted using the encryption key to obtain the identity ciphertext; The identity ciphertext is divided into multiple ciphertext fragments, and the multiple ciphertext fragments are associated with the derived fragments of the dynamic obfuscation factor and stored on different storage nodes, wherein the derived fragments stored on any storage node alone cannot reconstruct the dynamic obfuscation factor; In response to a data access request, after successful verification, the multiple encrypted fragments and their associated derived fragments are obtained from the different storage nodes; The dynamic obfuscation factor is reconstructed based on the derived fragments, the integrity of the ciphertext fragments is verified, and the identity ciphertext is reassembled based on the multiple ciphertext fragments. Obtain the corresponding decryption key based on the requester's permission level, decrypt the reconstructed identity ciphertext, and restore the user's identity data.

[0005] In one embodiment, obtaining the user's identity fingerprint includes: Collect biometric information from the user identity data to generate raw biometric data; The raw biometric data is input into a pre-trained convolutional neural network model to extract feature vectors; An irreversible hash algorithm is applied to the feature vector to generate a hash value, which serves as the identity fingerprint.

[0006] In one embodiment, the process of blinding the identity fingerprint to generate a blinded identity identifier, and simultaneously generating a dynamic obfuscation factor bound to the blinded identity identifier, includes: Generate random blinding factor; The identity fingerprint is modulo-added with the random blinding factor to obtain the blinded identity identifier; The randomization factor is input into the pseudo-random number generator to obtain the initial confusion factor; The blinded identity identifier is XORed with the initial obfuscation factor to obtain the dynamic obfuscation factor bound to the blinded identity identifier.

[0007] In one embodiment, generating the encryption key based on the blinded identity includes: Retrieve the system master key and current timestamp; The blind identity identifier, the system master key, and the current timestamp are concatenated and input into the HMAC-based key derivation function HKDF to generate the encryption key.

[0008] In one embodiment, the step of dividing the identity ciphertext into multiple ciphertext fragments and associating the multiple ciphertext fragments with the derived fragments of the dynamic obfuscation factor and storing them on different storage nodes includes: The ciphertext is divided into N ciphertext fragments using a ciphertext threshold secret sharing algorithm. At least K ciphertext fragments are needed to recover the ciphertext, where N>K≥1. The dynamic obfuscation factor is divided into M derivative fragments using a derivative threshold secret sharing algorithm, wherein at least L derivative fragments are required to reconstruct the dynamic obfuscation factor, M>L≥2, and L≥K; The N ciphertext fragments are associated one-to-one with the M derived fragments to generate N association pairs, where N=M; The N association pairs are stored on N geographically isolated storage nodes, such that the number of derived fragments stored individually on any one of the storage nodes is less than L.

[0009] In one embodiment, reconstructing the dynamic obfuscation factor based on the derived fragments and verifying the integrity of the ciphertext fragments includes: At least L derived fragments are obtained from the storage node, and the dynamic obfuscation factor is reconstructed based on the derived threshold secret sharing algorithm; Obtain at least L ciphertext fragments associated with the at least L derived fragments from the storage node; The integrity check value is calculated for each ciphertext fragment using the reconstructed dynamic obfuscation factor; The calculated integrity check value is compared with the pre-stored check value. If they are all consistent, the ciphertext fragment is confirmed to be complete and correctly associated. The pre-stored check value is calculated and stored in advance based on the dynamic confusion factor for each ciphertext fragment after it is divided into N ciphertext fragments. The identity ciphertext is recovered from the at least L ciphertext fragments based on the ciphertext threshold secret sharing algorithm.

[0010] In one embodiment, obtaining the corresponding decryption key based on the requester's permission level, decrypting the reconstructed identity ciphertext, and restoring the user identity data includes: When the requester's permission level is the data permission level, obtain the data decryption key and use the data decryption key to decrypt all data fields in the identity ciphertext; When the requester's permission level is the attribute permission level, obtain the attribute decryption key and the attribute mapping key, wherein the attribute permission level is associated with a predefined set of attributes, and the attribute decryption key is used to decrypt the ciphertext field corresponding to the attribute set; Based on the attribute mapping key, locate and extract the ciphertext field corresponding to the attribute set from the identity ciphertext; The encrypted field is decrypted using the attribute decryption key to recover the field in the user identity data that corresponds to the attribute set.

[0011] Secondly, a user identity data encryption / decryption system based on big data is provided, the system comprising: The fingerprint acquisition module is used to acquire the user's identity fingerprint; The blinding processing module is used to perform blinding processing on the identity fingerprint, generate a blinded identity identifier, and simultaneously generate a dynamic obfuscation factor bound to the blinded identity identifier; A key generation module is used to generate an encryption key based on the blinded identity identifier; An encryption module is used to encrypt user identity data using the encryption key to obtain ciphertext identity data; The sharded storage module is used to divide the identity ciphertext into multiple ciphertext shards, and associate the multiple ciphertext shards with the derived shards of the dynamic obfuscation factor and store them on different storage nodes, wherein the derived shards stored on any storage node alone cannot reconstruct the dynamic obfuscation factor; The access control module is used to verify the permissions of the requester in response to data access requests; The data acquisition module is used to acquire the multiple encrypted fragments and their associated derived fragments from the different storage nodes after verification. The reconstruction verification module is used to reconstruct the dynamic obfuscation factor based on the derived fragments, verify the integrity of the ciphertext fragments, and reassemble the identity ciphertext based on the multiple ciphertext fragments. The decryption module is used to obtain the corresponding decryption key based on the requester's permission level, decrypt the reconstructed identity ciphertext, and restore the user identity data.

[0012] Thirdly, a computer device is provided, including a memory and a processor, wherein the memory is communicatively connected to the processor, and the memory stores a computer program that can run on the processor, wherein when the processor executes the computer program, it implements the above-described method for encrypting and decrypting user identity data based on big data.

[0013] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the above-described method for encrypting and decrypting user identity data based on big data.

[0014] The aforementioned AI-based multi-source data fusion processing credential center management platform and method, by acquiring user identity fingerprints and performing blinding processing on these fingerprints to generate blinded identity identifiers, simultaneously generates a dynamic obfuscation factor bound to them. This achieves the independence of user identity information from subsequent encryption processes. Even if an attacker obtains fragmented data in the storage node, they cannot establish a connection between the fragments and the original identity through analysis. By dividing the ciphertext into multiple ciphertext fragments and storing them as derivative fragments of the dynamic obfuscation factor on different storage nodes, and ensuring that derivative fragments stored independently on any storage node cannot reconstruct the dynamic obfuscation factor, this method achieves... A dual-threshold protection mechanism is implemented, ensuring that a single compromised node cannot simultaneously meet the threshold conditions required for reconstructing the dynamic obfuscation factor and recovering the ciphertext. During data access, the integrity of the acquired ciphertext fragments is verified based on the reconstructed dynamic obfuscation factor, effectively detecting whether fragments have been tampered with or replaced, ensuring the integrity and reliability of the fragment data participating in the reconstruction. At the same time, the corresponding decryption key is obtained based on the requester's permission level, providing differentiated data field decryption capabilities for users with different permission levels, achieving fine-grained access control. This not only meets the data usage needs under different business scenarios but also maximizes the security of user identity data. Attached Figure Description

[0015] Figure 1 This is an application environment diagram of a user identity data encryption and decryption method based on big data in one embodiment; Figure 2 This is a flowchart illustrating a user identity data encryption / decryption method based on big data in one embodiment; Figure 3 This is a schematic diagram of the process for obtaining an identity fingerprint in one embodiment; Figure 4 This is a schematic diagram of the process for generating a blinded identity and a dynamic obfuscation factor in one embodiment; Figure 5 This is a schematic diagram of the process for generating an encryption key in one embodiment; Figure 6 This is a schematic diagram illustrating the secure segmentation and associated storage of identity ciphertext and dynamic obfuscation factors in one embodiment; Figure 7 This is a flowchart illustrating the process of verifying the integrity of encrypted fragments in one embodiment; Figure 8 This is a schematic diagram of the process for restoring user identity data in one embodiment; Figure 9 This is a schematic diagram of the structure of a user identity data encryption and decryption system based on big data in one embodiment; Figure 10 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0016] To facilitate understanding of the technical solutions provided in the embodiments of this application, the background technology involved in the embodiments of this application will be described below.

[0017] With the rapid development of big data, cloud computing, and IoT technologies, the amount of user identity data accumulated across various industries is growing exponentially. To cope with the storage pressure of massive amounts of data and improve system scalability, distributed storage architecture has become the mainstream choice. Under this architecture, data sharding technology is widely used, which involves dividing large-scale datasets into multiple smaller data shards and distributing them across different physical nodes to achieve load balancing and high availability.

[0018] However, this method of storing data in fragmented form, while solving the storage scale problem, also introduces new security challenges. On the one hand, distributed data storage increases the risk of data correlation analysis. Although a single data shard may not contain complete user identity information, attackers can obtain shards from multiple storage nodes, combine them with metadata or access pattern analysis, and reassemble the scattered data fragments to reconstruct complete user identity information. This phenomenon is known as the identity reconstruction problem. In other words, while sharded storage avoids the leakage of all data at a single point, it fails to effectively sever the correlation between shards and user identities. Attackers can still breach privacy protection barriers through correlation analysis.

[0019] On the other hand, ensuring the integrity of fragmented data becomes more difficult. In a distributed environment, data fragments can be corrupted due to storage media failures, network transmission errors, or malicious tampering, and such corruption is often difficult to detect in a timely manner. Traditional integrity verification schemes, such as Proof of Data Holding (PDP) and Proof of Retrievability (PoR) schemes, can verify data integrity to a certain extent, but most schemes rely on complex public-key cryptographic operations and are often unable to effectively address data tampering issues in scenarios involving collusion attacks among storage nodes or dynamic updates. Existing research indicates that many integrity auditing schemes still have shortcomings in terms of security and availability, making them difficult to directly apply to distributed storage systems with high information theory security requirements.

[0020] Furthermore, in complex application scenarios involving multiple users and multiple services, how to achieve fine-grained access control over user identity data is also a problem that urgently needs to be solved. Requesters with different roles and permissions may need to access different fields in the same user data, but existing solutions often adopt a coarse-grained decryption method where the decryption result is either all or nothing, making it difficult to maximize the protection of user privacy while meeting business needs.

[0021] In light of this, effectively severing the correlation between fragmented data and user identities while implementing distributed storage of user identity data, ensuring the integrity and credible source of each fragment, and providing flexible field-level access control capabilities have become core technical problems urgently needing to be solved in the field of user identity data protection. Therefore, this application provides a method, system, computer device, and storage medium for encrypting and decrypting user identity data based on big data.

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0023] It should be noted that, in the embodiments of this application, all activities involving the collection, storage, and use of user identity data must be carried out in compliance with relevant laws and regulations, and must obtain the user's explicit authorization and consent in advance to ensure the legality, legitimacy, and necessity of the data processing activities. The technical solutions in the embodiments of this application are only to further protect data security and privacy from a technical perspective, based on the legal authorization already obtained.

[0024] The user identity data encryption and decryption method based on big data provided in this application can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. Terminal 102 acquires a user's identity fingerprint and sends it to server 104. Server 104 performs blinding processing on the received user identity fingerprint to generate a blinded identity identifier and simultaneously generates a dynamic obfuscation factor bound to the blinded identity identifier. Based on the blinded identity identifier, an encryption key is generated, and the user's identity data is encrypted using the encryption key to obtain identity ciphertext. The identity ciphertext is divided into multiple ciphertext fragments, and these fragments are associated with and stored on different storage nodes as derived fragments of the dynamic obfuscation factor. The derived fragments stored on any single storage node cannot reconstruct the dynamic obfuscation factor. When user identity data needs to be accessed, terminal 102 sends a data access request to server 104. After verifying the requester's permissions, server 104 obtains multiple ciphertext fragments and their associated derived fragments from different storage nodes; it reconstructs a dynamic obfuscation factor based on the derived fragments, verifies the integrity of the ciphertext fragments, and reassembles the identity ciphertext based on multiple ciphertext fragments; it obtains the corresponding decryption key based on the requester's permission level, decrypts the reassembled identity ciphertext, restores the user identity data, and returns the restored user identity data to terminal 102. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, portable wearable devices, or biometric collection devices. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers. Storage nodes can be standalone storage servers or cloud storage nodes.

[0025] First, a brief explanation of the terms and technologies that may be involved in the embodiments of this application is provided. In the embodiments of this application, "identity fingerprint" refers to a digital feature that can uniquely identify a user's identity. It can be generated based on the user's biometric information, the hardware information of the device held by the user, or a combination of the above information. "Threshold secret sharing algorithm" is a cryptographic method that splits a secret into multiple shares (called fragments or shadows). Its core characteristic is that at least a preset number (threshold value) of shares is required to reconstruct the original secret, while shares less than the threshold value cannot obtain any information about the secret. A classic implementation scheme includes the Shamir secret sharing algorithm, which is constructed based on the Lagrange interpolation theorem.

[0026] Firstly, in existing data sharding storage solutions, although attackers cannot directly obtain complete user identity information from a single storage node, they can gradually establish the relationship between shards and user identity by analyzing the data sharding and metadata access patterns of multiple nodes, and then deduce the complete user identity information, which means there is a security risk of identity reconstruction.

[0027] Therefore, in one embodiment, a user identity data encryption / decryption method based on big data is provided, which is applied to... Figure 1 Taking the terminal in the example of this, for example... Figure 2 As shown, the specific steps include: Step S1: Obtain the user's identity fingerprint. In this step, the user's identity fingerprint refers to a digital digest used to uniquely identify the user. It can be a feature value obtained by processing the user's biometric features (such as fingerprint images, facial images, voiceprints, etc.), or a hash value generated based on the user's device hardware information (such as device serial number, International Mobile Equipment Identity (IMEI), etc.). In practical applications, the user's identity fingerprint can be generated by collecting the user's biometric information through the terminal device and processing it locally, or it can be calculated and generated by the server after receiving the user's identity data.

[0028] Step S2: Perform blinding processing on the identity fingerprint to generate a blinded identity identifier, and simultaneously generate a dynamic obfuscation factor bound to the blinded identity identifier. In this step, blinding processing refers to transforming the original identity fingerprint into a new blinded identity identifier by introducing randomness, making it impossible for an attacker to reverse-engineer the original identity fingerprint even if they obtain the blinded identity identifier. The dynamic obfuscation factor is a random data string mathematically bound to the blinded identity identifier. Its function is to establish an association with the ciphertext fragment during subsequent storage, thereby severing the direct link between the ciphertext fragment and the user's identity.

[0029] Step S3: Generate an encryption key based on the blinded identity identifier, and use this key to encrypt the user identity data to obtain ciphertext. In this step, the generation of the encryption key relies on the blinded identity identifier. Because the blinded identity identifier itself is random and not directly related to the original identity fingerprint, the generated encryption key is also dynamic and unpredictable. Existing symmetric encryption algorithms, such as AES-256, can be used. User identity data refers to personal information that needs to be protected, such as name, ID card number, mobile phone number, bank card number, etc. Through encryption, the original user identity data is converted into unreadable ciphertext.

[0030] Step S4: Divide the identity ciphertext into multiple ciphertext fragments, and associate these fragments with derived fragments of the dynamic obfuscation factor, storing them on different storage nodes. A derived fragment stored alone on any single storage node cannot reconstruct the dynamic obfuscation factor. In this step, the identity ciphertext is divided into multiple data blocks, each called a ciphertext fragment. Simultaneously, the dynamic obfuscation factor is also divided into multiple data blocks, each called a derived fragment. The ciphertext fragments and derived fragments are associated in a one-to-one correspondence, forming multiple association pairs. These association pairs are then distributed and stored on different, geographically isolated storage nodes. Because the dynamic obfuscation factor itself is stored in segments, and each storage node only holds one derived fragment, the number of derived fragments stored on a single node is insufficient to reconstruct the complete dynamic obfuscation factor, thus ensuring the confidentiality of the dynamic obfuscation factor.

[0031] Step S5: In response to a data access request, after successful verification, retrieve multiple encrypted fragments and their associated derived fragments from different storage nodes. In this step, when a legitimate requester needs to access user identity data, their identity and permissions must first be verified. Only requests that pass verification are allowed to read the encrypted fragments and corresponding derived fragments related to this access from various storage nodes.

[0032] Step S6: Reconstruct the dynamic obfuscation factor based on derived fragments, verify the integrity of the ciphertext fragments, and reassemble the identity ciphertext based on multiple ciphertext fragments. In this step, the obtained derived fragments sufficient to reconstruct the dynamic obfuscation factor are combined to recover the complete dynamic obfuscation factor. Then, the integrity of the obtained ciphertext fragments is verified using the recovered dynamic obfuscation factor to ensure that these ciphertext fragments have not been tampered with or replaced during storage or transmission. After the integrity verification passes, the ciphertext fragments are combined to recover the complete identity ciphertext.

[0033] Step S7: Obtain the corresponding decryption key based on the requester's permission level, decrypt the reconstructed identity ciphertext, and recover the user identity data. In this step, different decryption keys are assigned according to the requester's permission level. For example, a requester with a higher permission level may obtain a key capable of decrypting all data fields, while a requester with a lower permission level may only obtain a key capable of decrypting some data fields, thus achieving fine-grained access control. Finally, use the decryption key to decrypt the identity ciphertext to obtain the original user identity data.

[0034] Based on the above, by combining the blind processing of user identity fingerprints, the associated storage of dynamic obfuscation factors and encrypted fragments, and the decryption mechanism based on permission levels, the associativity between fragmented data and user identity is effectively severed, and fragment integrity verification and fine-grained access control functions are provided, thereby improving the security of user identity data in a distributed storage environment.

[0035] To extract a unique and stable identity fingerprint from user identity data, in one embodiment, the user identity fingerprint is obtained, such as... Figure 3 As shown, the specific steps include: Step S11: Collect biometric information from user identity data to generate raw biometric data. In this step, the biometric information in the user identity data may include, but is not limited to, fingerprint images, face images, iris images, and voiceprint data. The raw signal data of these biometric features, such as grayscale images of fingerprints or audio files of voiceprints, is acquired through appropriate sensors or acquisition devices (such as fingerprint scanners, cameras, microphones, etc.).

[0036] Step S12: Input the raw biometric data into a pre-trained convolutional neural network model to extract feature vectors. In this step, the convolutional neural network model is a deep learning model, whose structure typically includes multiple convolutional layers, pooling layers, and fully connected layers. In this embodiment, the model is pre-trained with a large amount of biometric sample data, enabling it to learn and extract unique and stable deep features from the input raw biometric data. These features can effectively distinguish different individual users. For example, for face images, the model can extract the geometric relationships and texture features of facial key points; for fingerprint images, the model can extract features such as ridge direction and minutiae distribution. The output of the convolutional neural network model is a fixed-dimensional feature vector, which is the mathematical representation of the raw biometric data.

[0037] Step S13: Apply an irreversible hash algorithm to the feature vector to generate a hash value as the identity fingerprint. In this step, the irreversible hash algorithm is a one-way function that can convert input data of arbitrary length into a fixed-length output value (hash value), and the original input data cannot be deduced from the hash value. Commonly used algorithms include SHA-256. The hash value obtained after calculating the hash value using the feature vector as input is the final identity fingerprint. This identity fingerprint is unique (feature vectors of different users are extremely unlikely to collide), has a fixed length, and is irreversible, facilitating subsequent storage and processing.

[0038] Based on the above, a convolutional neural network model is used to automatically extract deep features of biometrics, and then combined with an irreversible hash algorithm to generate identity fingerprints. This ensures both the uniqueness and distinguishability of the identity fingerprints and the security of the original biometric data, preventing the risk of biometric leakage. In practical applications, the specific structure of the convolutional neural network model can be adjusted according to the type of biometric feature; this application does not limit this.

[0039] To achieve blinding processing of identity fingerprints and generate a dynamic obfuscation factor bound to them, in one embodiment, the identity fingerprint is blinded to generate a blinded identity identifier, and a dynamic obfuscation factor bound to the blinded identity identifier is generated simultaneously, such as... Figure 4 As shown, the specific steps include: Step S21: Generate a random blinding factor. In this step, the random blinding factor is a random number string generated by a true random number generator or a cryptographically secure pseudo-random number generator. Its length can be the same as the length of the identity fingerprint to ensure the security and reversibility of subsequent operations.

[0040] Step S22: Perform a modular addition operation on the identity fingerprint and the random blinding factor to obtain the blinded identity identifier. In this step, modular addition refers to addition operations performed within a finite field or integer modulo space. Assume the identity fingerprint is... The random blinding factor is Then blind identity identification It can be done through formula The calculation yielded, where It is the modulus, usually taken as... , This is the bit length of the identity fingerprint. This operation makes the original identity fingerprint... Hidden in random numbers (random blinding factor) In this context, even if the attacker obtains a blinded identity... Since the random blinding factor is unknown It is also impossible to recover the identity fingerprint. .

[0041] Step S23: Input the random blinding factor into the pseudo-random number generator to obtain the initial confusion factor. In this step, the pseudo-random number generator (PRNG) is a deterministic algorithm capable of expanding the input seed value into a bit sequence of arbitrary length with approximately random characteristics. In this embodiment, the random blinding factor... As a seed for a PRNG, a blinded identity identifier is generated through the PRNG. Random sequences of the same length are used as initial confusion factors. This step ensures the initial confusion factor. With random blinding factor It has a definite derivation relationship.

[0042] Step S24: Perform an XOR operation between the blinded identity identifier and the initial obfuscation factor to obtain the dynamic obfuscation factor bound to the blinded identity identifier. In this step, the Exclusive OR (XOR) operation is a bitwise logical operation, with the symbol _____. The operation rule is that the same value is 0, and different values ​​are 1. Dynamic Confusion Factor It can be done through formula Calculated. Due to the reversibility of the XOR operation and the blinded identity verification... and initial confusion factor All are related to the random blinding factor Related, therefore the generated dynamic obfuscation factor With blind identity identification There is a close mathematical bond between them. Specifically, the formula can be expanded as follows: , This refers to generating the initial confusion factor using a pseudo-random number generator (PRNG), which is visible as a dynamic confusion factor. It is an identity fingerprint and random blinding factor The composite function value, and cannot be obtained without knowing the random blinding factor. In some cases, it is disassembled separately.

[0043] Based on the above, a blind identity identifier was generated. and the dynamic obfuscation factor associated with it This binding relationship ensures that, during subsequent storage and verification, the association between ciphertext fragments and derived fragments is based on an indivisible mathematical whole, rather than a simple logical association that can be broken individually, thus enhancing security.

[0044] To generate dynamic and secure encryption keys, in one embodiment, encryption keys are generated based on a blinded identity, such as... Figure 5 As shown, the specific steps include: Step S31: Obtain the system master key and current timestamp. In this step, the system master key is a highly confidential global key generated and maintained by the Key Management System (KMS), used as the base key material for key derivation. The current timestamp is a numerical value representing the current time, typically accurate to the second or millisecond, used to increase key dynamism and prevent replay attacks. The introduction of the timestamp ensures that even in multiple encryption requests from the same user, the generated encryption key will be different due to the different timestamps, thereby improving key security.

[0045] Step S32: Concatenate the blinded identity, system master key, and current timestamp, and input the result into the HMAC-based key derivation function HKDF to generate the encryption key. In this step, HKDF (HMAC-based Key Derivation Function) is a standardized key derivation function based on hash message authentication codes, known for its high security and widespread use in cryptographic practice. HKDF typically includes an extraction step and an expansion step. In this embodiment, the concatenated string of the blinded identity, system master key, and current timestamp is first used as the input key material and input into the extraction step of HKDF. This step outputs a fixed-length, uniformly random intermediate key. Subsequently, the intermediate key and optional context information are input into the expansion step to expand the final encryption key to a specified length. The concatenation operation can use simple string concatenation, such as joining the three parts in a fixed order. The standard definition of the HMAC-based key derivation function HKDF can be found in RFC 5869 (this document provides a detailed description and translation of the HMAC-based key derivation function (HKDF)).

[0046] Based on the above, an encryption key closely related to the current user, the current time, and the system's global secret is generated. This key has the following characteristics: First, due to the participation of the blinded identity identifier, the key is bound to a specific user; second, due to the participation of the timestamp, the key has a time limit; finally, due to the participation of the system master key, the key's security is globally guaranteed. In practical applications, the specific implementation of HKDF can use existing cryptographic libraries, such as OpenSSL. For the storage and management of the system master key, mature solutions such as Hardware Security Module (HSM) or Cloud Key Management Service (KMS) can be used, and this application embodiment does not limit this.

[0047] To achieve secure segmentation and associated storage of identity ciphertext and dynamic obfuscation factors, in one embodiment, the identity ciphertext is segmented into multiple ciphertext fragments, and these fragments are associated with and stored as derived fragments of the dynamic obfuscation factors on different storage nodes, such as... Figure 6 As shown, the specific steps include: Step S41: Divide the ciphertext into N ciphertext fragments using a ciphertext threshold secret sharing algorithm. At least K ciphertext fragments are needed to recover the ciphertext, where N > K ≥ 1. In this step, the ciphertext threshold secret sharing algorithm can employ the classic Shamir secret sharing scheme. This scheme constructs a... polynomial of degree The constant term Set as protected identity ciphertext The remaining coefficients Randomly select from a finite field. Then, select N distinct non-zero elements. As an identifier, calculate the polynomial value corresponding to each identifier. As a ciphertext fragment Thus, any K distinct ciphertext fragments The polynomial can then be reconstructed using Lagrange interpolation. Thus, the constant term is recovered. That is, identity cipher However, if fewer than K fragments are available, it will be impossible to obtain the ciphertext regarding the identity. Any information.

[0048] Step S42: Divide the dynamic obfuscation factor into M derived fragments using the derived threshold secret sharing algorithm. Reconstructing the dynamic obfuscation factor requires at least L derived fragments, where M > L ≥ 2 and L ≥ K. Similarly, the Shamir secret sharing scheme can be used in this step. Construct a... polynomial of degree Dynamic confusion factor Let it be a constant term. Randomly select other coefficients. Select M distinct non-zero elements. (can be with) (Same or different), calculation As a derivative fragment This embodiment limits L≥K to ensure that the number of derived fragments (at least L) obtained for reconstructing the dynamic obfuscation factor in the subsequent recovery process can also meet the number of ciphertext fragments (at least K) required to recover the identity ciphertext, thereby simplifying the data acquisition logic.

[0049] Step S43: Associate the N ciphertext fragments with the M derived fragments to generate N association pairs, where N=M. In this step, setting N and M to be equal is to achieve a one-to-one association relationship. Specifically, the Nth... A ciphertext fragment With the A derivative fragment Combined together, they form an association pair. ,in The value range is from 1 to N.

[0050] Step S44: Store the N association pairs on N geographically isolated storage nodes, such that the number of derived shards stored on any single storage node is less than L. In this step, the association pairs... Stored as a whole in the first On each storage node. Since each node stores only one association pair, that is, each storage node stores only one derived shard. According to the definition of threshold secret sharing, at least L derived fragments (L≥2) are needed to reconstruct the dynamic obfuscation factor. Therefore, the number (1) of a single derived fragment stored on any storage node is much smaller than the required threshold L, making it impossible to independently reconstruct the dynamic obfuscation factor. Even if an attacker breaches a single storage node, they can only obtain one association pair, unable to recover the identity ciphertext (because K ciphertext fragments are needed) or reconstruct the dynamic obfuscation factor (because L derived fragments are needed), which increases the difficulty of the attack. Geographical isolation of storage nodes can further reduce the risk of multiple nodes being simultaneously affected by physical attacks or regional disasters.

[0051] Based on the above, the ciphertext and dynamic obfuscation factor are segmented using different threshold schemes and stored together on different nodes. This dual-threshold and associated storage mechanism, by setting different threshold values, strictly limits the amount of information leaked if a single node is compromised to below the threshold value, thus preventing it from posing a substantial threat to data security. In other words, it ensures that even if an attacker compromises some nodes, they cannot simultaneously meet the threshold requirements for reconstructing the ciphertext and dynamic obfuscation factor, thereby achieving high-strength protection for user identity data.

[0052] To implement the process of retrieving data from storage nodes and verifying its integrity, in one embodiment, the integrity of the ciphertext fragments is verified by reconstructing a dynamic obfuscation factor based on derived fragments, such as... Figure 7 As shown, the specific steps include: Step S61: Obtain at least L derived shards from the storage nodes and reconstruct the dynamic obfuscation factor based on the derived threshold secret sharing algorithm. In this step, when data access is initiated, data is requested from each storage node. According to the threshold requirements, at least L derived shards need to be collected. For example, L storage nodes can be randomly selected or selected according to a certain strategy, and the associated pairs stored on these nodes can be obtained, from which L derived shards can be extracted. ,in This represents the set of indexes for the selected storage node. Indicates from the first Each storage node acquires a derived fragment. Then, these derived fragments and their corresponding node identifiers are used... ,in Indicates the first A derivative fragment In the derived threshold secret sharing algorithm, the corresponding node identifier is reconstructed into a polynomial using Lagrange interpolation. Then, the constant term is extracted from it, which yields the dynamic confusion factor. .

[0053] Step S62: Obtain at least L ciphertext fragments associated with at least L derived fragments from the storage nodes. In this step, while obtaining the derived fragments, the L ciphertext fragments associated with them are also obtained from the same L storage nodes. ,in, Indicates from the first Ciphertext fragments obtained from each storage node.

[0054] Step S63: Calculate the integrity check value for each ciphertext fragment using the reconstructed dynamic obfuscation factor. In this step, the integrity check value can be calculated in various ways. In this embodiment, the following method for calculating the check value based on the dynamic obfuscation factor is used, specifically: Assuming the ciphertext is fragmented as follows Its corresponding derived fragments are The reconstructed dynamic obfuscation factor is Define integrity check value The calculation formula is: in, It is a cryptographic hash function (such as SHA-256). This indicates a bitwise XOR operation. It is a dynamic confusion factor Extracting from the first A function for the subkey corresponding to each ciphertext fragment, which can be defined as: That is, using dynamic obfuscation factors As a key, index the node. Performing an HMAC operation yields a fixed-length pseudo-random bit string. The length of this pseudo-random bit string is related to the ciphertext fragmentation. The lengths of the ciphertext fragments are the same, used for XOR operations. This calculation formula ensures that the generation of the checksum depends not only on the ciphertext fragments themselves, but also closely on the dynamic obfuscation factor and the fragment index, so that any tampering with the ciphertext fragments or derived fragments will result in the final calculated checksum being inconsistent with the original value.

[0055] Step S64: Compare the calculated integrity check value with the pre-stored check value. If they match, the ciphertext fragments are confirmed to be complete and correctly associated. The pre-stored check value is calculated and stored in advance for each fragment after it is divided into N fragments based on a dynamic obfuscation factor. In this step, each fragment has already been calculated according to the formula of the above-mentioned check value calculation method based on the dynamic obfuscation factor during the data segmentation and storage stage. Initial integrity check value The data is then securely stored, either on a trusted metadata management node or encrypted and stored on a storage node along with its associated pairs. During the data acquisition and reconstruction phases, the system uses the same method to calculate the checksum of the currently acquired ciphertext fragment. and combine it with the pre-stored Each ciphertext fragment is compared one by one. Only when the current checksum of all L ciphertext fragments is completely consistent with the original checksum can it be confirmed that these ciphertext fragments have not been tampered with or replaced during storage and transmission, and that the association between them and the derived fragments is correct.

[0056] Step S65: Recover the identity ciphertext from at least L ciphertext fragments using the ciphertext threshold secret sharing algorithm. In this step, since L≥K, the obtained L ciphertext fragments already satisfy the threshold requirement for recovering the identity ciphertext. Utilize these L ciphertext fragments... and its corresponding node identifier ,in Indicates the first A ciphertext fragment The node identifier corresponding to the ciphertext threshold secret sharing algorithm is reconstructed into a polynomial using Lagrange interpolation. And recover the ciphertext of the identity from it. .

[0057] Based on the above, not only can the dynamic obfuscation factor and identity ciphertext be reconstructed, but the integrity and correlation of the ciphertext fragments can also be verified before recovery. This mechanism of verification before recovery effectively prevents attackers from interfering with the system or obtaining information by providing forged or tampered data fragments, ensuring the authenticity and reliability of the finally decrypted user identity data. In practical applications, the storage of integrity verification values ​​also needs to be secured to prevent malicious tampering; this application does not impose any limitations on this aspect.

[0058] To achieve fine-grained access control based on user permission levels, in one embodiment, the corresponding decryption key is obtained based on the requester's permission level, the reconstructed identity ciphertext is decrypted, and the user identity data is recovered. Figure 8 As shown, the specific steps include: Step S71: When the requester's permission level is the data permission level, obtain the data decryption key and use it to decrypt all data fields in the identity ciphertext. In this step, the data permission level is the highest predefined level of permission, typically granted to system administrators or data owners. A requester with this level of permission can obtain the complete data decryption key, which can decrypt all fields in the reconstructed identity ciphertext, thereby obtaining the user's complete identity data.

[0059] Step S72: When the requester's permission level is the attribute permission level, obtain the attribute decryption key and attribute mapping key. The attribute permission level is associated with a predefined set of attributes, and the attribute decryption key is used to decrypt the ciphertext fields corresponding to the attribute set. In this step, the attribute permission level is a predefined lower-level permission, typically granted to business personnel or third-party applications that need access to certain data. Each attribute permission level is associated with a predefined set of attributes, which specifies which data fields can be accessed at that level. For example, the "Basic Information" level can access names and phone numbers, and the "Health Information" level can access medical examination reports and medical records. The attribute decryption key is specifically used to decrypt these specific attribute fields, while the attribute mapping key is a metadata key that guides how to locate and extract these fields from the identity ciphertext.

[0060] Step S73: Locate and extract the ciphertext fields corresponding to the attribute set from the identity ciphertext based on the attribute mapping key. In this step, the identity ciphertext contains identification information for each field in its data structure during encryption. The attribute mapping key can be decrypted (e.g., using the requester's own private key) to obtain a field position mapping table, indicating the starting position and length of each attribute field in the identity ciphertext. Based on this mapping table and the attribute set associated with the requester's permission level, the system can accurately extract the required encrypted ciphertext field fragments from the identity ciphertext.

[0061] Step S74: Decrypt the ciphertext fields using the attribute decryption key to recover the fields in the user identity data corresponding to the attribute set. In this step, the extracted ciphertext field fragments are decrypted using the attribute decryption key to obtain the plaintext data of the field, which is then returned to the requester. For fields outside the requester's authorization scope, the system will not perform any decryption operations; these fields remain in ciphertext form throughout memory and transmission.

[0062] Based on the above, differentiated data disclosure is achieved for users with different access levels using the same encrypted identity. Users with data access levels can see the complete user profile, while users with attribute access levels can only see fields relevant to their business. This mechanism refines data access granularity from the entire file to the field level, adhering to the principle of data minimization and maximizing user privacy while meeting business needs. In practical applications, the distribution and management of attribute mapping keys and attribute decryption keys can be implemented in conjunction with attribute-based encryption or key management systems; this application does not limit this approach.

[0063] It should be understood that, although Figure 2-8 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2-7 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0064] Secondly, this embodiment provides a user identity data encryption and decryption system based on big data, such as... Figure 9 As shown, it includes a fingerprint acquisition module, a blinding processing module, a key generation module, an encryption module, a fragmented storage module, an access control module, a data acquisition module, a reconstruction verification module, and a decryption module.

[0065] The fingerprint acquisition module is used to acquire the user's identity fingerprint. This module is responsible for receiving or generating digital fingerprint information that uniquely identifies the user.

[0066] The blinding processing module is used to blind the identity fingerprint, generate a blinded identity identifier, and simultaneously generate a dynamic obfuscation factor bound to the blinded identity identifier. This module transforms the identity fingerprint by introducing randomness and generates a dynamic obfuscation factor that has a mathematical correlation with the blinding result.

[0067] The key generation module is used to generate encryption keys based on blinded identity identifiers. This module dynamically generates keys for encrypting user identity data based on the blinded identity identifiers and system security parameters.

[0068] The encryption module is used to encrypt user identity data using an encryption key, resulting in ciphertext. This module employs a symmetric encryption algorithm to convert the original user identity data into ciphertext.

[0069] The sharding storage module is used to divide the identity ciphertext into multiple ciphertext shards and associate these ciphertext shards with derived shards of a dynamic obfuscation factor, storing them on different storage nodes. A derived shard stored on any single storage node cannot reconstruct the dynamic obfuscation factor. This module is responsible for thresholding the identity ciphertext and dynamic obfuscation factor, and distributing the generated association pairs to multiple geographically isolated storage nodes, ensuring that the data on a single node is insufficient to recover the complete secret.

[0070] The access control module is used to respond to data access requests and verify the requester's permissions. This module authenticates and verifies the identity and permissions of the requester initiating data access, allowing only successfully verified requests to proceed to subsequent processes.

[0071] The data acquisition module is used to retrieve multiple encrypted fragments and their associated derived fragments from different storage nodes after successful verification. This module retrieves a sufficient number of associated data pairs from multiple storage nodes in parallel, based on preset threshold requirements.

[0072] The reconstruction and verification module is used to reconstruct the dynamic obfuscation factor based on derived fragments, verify the integrity of the ciphertext fragments, and reassemble the identity ciphertext based on multiple ciphertext fragments. This module first recovers the dynamic obfuscation factor using the acquired derived fragments, then uses the recovered dynamic obfuscation factor to verify the integrity of the ciphertext fragments. After confirming that the data has not been tampered with, it reassembles the complete identity ciphertext.

[0073] The decryption module is used to obtain the corresponding decryption key based on the requester's permission level, decrypt the reconstructed ciphertext, and recover the user's identity data. This module retrieves the appropriate decryption key from the key management system according to the requester's permission level, decrypts the ciphertext, and finally outputs the original user identity data.

[0074] The aforementioned modules work together to achieve secure storage and fine-grained access control of user identity data. Specific limitations regarding the big data-based user identity data encryption and decryption system can be found in the above section on the limitations of big data-based user identity data encryption and decryption methods, and will not be repeated here. Each module in the aforementioned big data-based user identity data encryption and decryption system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0075] In one embodiment, the fingerprint acquisition module includes a fingerprint acquisition unit.

[0076] The fingerprint acquisition unit is used to collect biometric information from user identity data and generate raw biometric data; to input the raw biometric data into a pre-trained convolutional neural network model to extract feature vectors; and to apply an irreversible hash algorithm to the feature vectors to generate a hash value as an identity fingerprint.

[0077] In one embodiment, the blinding processing module includes a blinding processing unit.

[0078] The blinding processing unit is used to generate a random blinding factor; to perform a modulo addition operation on the identity fingerprint and the random blinding factor to obtain a blinded identity identifier; to input the random blinding factor into a pseudo-random number generator to obtain an initial confusion factor; and to perform an XOR operation on the blinded identity identifier and the initial confusion factor to obtain a dynamic confusion factor bound to the blinded identity identifier.

[0079] In one embodiment, the key generation module includes a key generation unit.

[0080] The key generation unit is used to obtain the system master key and the current timestamp; and to concatenate the blinded identity identifier, the system master key and the current timestamp and input them into the HMAC-based key derivation function HKDF to generate an encryption key.

[0081] In one embodiment, the fragmented storage module includes fragmented storage units.

[0082] The segmented storage unit is used to divide the ciphertext into N ciphertext segments using a ciphertext threshold secret sharing algorithm, where recovering the ciphertext requires at least K ciphertext segments, and N>K≥1; it is used to divide the dynamic obfuscation factor into M derivative segments using a derivative threshold secret sharing algorithm, where reconstructing the dynamic obfuscation factor requires at least L derivative segments, and M>L≥2, and L≥K; it is used to generate N association pairs by associating the N ciphertext segments with the M derivative segments in a one-to-one correspondence, where N=M; and it is used to store the N association pairs on N geographically isolated storage nodes, so that the number of derivative segments stored on any single storage node is less than L.

[0083] In one embodiment, the reconstructed verification module includes a reconstructed verification unit.

[0084] The reconstruction verification unit is used to obtain at least L derived fragments from the storage node and reconstruct the dynamic obfuscation factor based on the derived threshold secret sharing algorithm; to obtain at least L ciphertext fragments associated with the at least L derived fragments from the storage node; to calculate the integrity check value for each ciphertext fragment using the reconstructed dynamic obfuscation factor; to compare the calculated integrity check value with the pre-stored check value, and if they are all consistent, to confirm that the ciphertext fragment is complete and correctly associated, wherein the pre-stored check value is calculated and stored in advance based on the dynamic obfuscation factor for each ciphertext fragment after it is divided into N ciphertext fragments; and to recover the identity ciphertext from the at least L ciphertext fragments based on the ciphertext threshold secret sharing algorithm.

[0085] In one embodiment, the decryption module includes a data decryption unit.

[0086] The data decryption unit is used to: obtain a data decryption key when the requester's permission level is data permission level, and use the data decryption key to decrypt all data fields in the identity ciphertext; obtain an attribute decryption key and an attribute mapping key when the requester's permission level is attribute permission level, wherein the attribute permission level is associated with a predefined set of attributes, and the attribute decryption key is used to decrypt the ciphertext fields corresponding to the attribute set; locate and extract the ciphertext fields corresponding to the attribute set from the identity ciphertext based on the attribute mapping key; and decrypt the ciphertext fields using the attribute decryption key to recover the fields in the user identity data corresponding to the attribute set.

[0087] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When executed by the processor, the computer program implements a user identity data encryption / decryption method based on large data sets. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or external biometric acquisition devices such as a keyboard, touchpad, mouse, or fingerprint scanner.

[0088] Those skilled in the art will understand that Figure 10The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0089] Thirdly, a computer device is provided, including a memory and a processor. The memory is communicatively connected to the processor, and the memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the user identity data encryption and decryption method based on big data as described above. Furthermore, the specific limitations of the computer device in implementing the user identity data encryption and decryption method based on big data can be found in the limitations of the user identity data encryption and decryption method based on big data described above, and will not be repeated here.

[0090] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the aforementioned user identity data encryption and decryption method based on big data. Furthermore, the specific limitations of the computer-readable storage medium in implementing the user identity data encryption and decryption method based on big data can be found in the limitations of the user identity data encryption and decryption method based on big data described above, and will not be repeated here.

[0091] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0092] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for encrypting and decrypting user identity data based on big data, characterized in that, The method includes: Obtain user identity fingerprint; The identity fingerprint is blinded to generate a blinded identity identifier, and a dynamic obfuscation factor bound to the blinded identity identifier is generated simultaneously. An encryption key is generated based on the blinded identity identifier, and the user identity data is encrypted using the encryption key to obtain the identity ciphertext; The identity ciphertext is divided into multiple ciphertext fragments, and the multiple ciphertext fragments are associated with the derived fragments of the dynamic obfuscation factor and stored on different storage nodes, wherein the derived fragments stored on any storage node alone cannot reconstruct the dynamic obfuscation factor; In response to a data access request, after successful verification, the multiple encrypted fragments and their associated derived fragments are obtained from the different storage nodes; The dynamic obfuscation factor is reconstructed based on the derived fragments, the integrity of the ciphertext fragments is verified, and the identity ciphertext is reassembled based on the multiple ciphertext fragments. Obtain the corresponding decryption key based on the requester's permission level, decrypt the reconstructed identity ciphertext, and restore the user's identity data.

2. The method according to claim 1, characterized in that, The process of obtaining the user's identity fingerprint includes: Collect biometric information from the user identity data to generate raw biometric data; The raw biometric data is input into a pre-trained convolutional neural network model to extract feature vectors; An irreversible hash algorithm is applied to the feature vector to generate a hash value, which serves as the identity fingerprint.

3. The method according to claim 1, characterized in that, The process of blinding the identity fingerprint to generate a blinded identity identifier, and simultaneously generating a dynamic obfuscation factor bound to the blinded identity identifier, includes: Generate random blinding factor; The identity fingerprint is modulo-added with the random blinding factor to obtain the blinded identity identifier; The randomization factor is input into the pseudo-random number generator to obtain the initial confusion factor; The blinded identity identifier is XORed with the initial obfuscation factor to obtain the dynamic obfuscation factor bound to the blinded identity identifier.

4. The method according to claim 1, characterized in that, The generation of the encryption key based on the blinded identity includes: Retrieve the system master key and current timestamp; The blind identity identifier, the system master key, and the current timestamp are concatenated and input into the HMAC-based key derivation function HKDF to generate the encryption key.

5. The method according to claim 1, characterized in that, The step of dividing the identity ciphertext into multiple ciphertext fragments and associating these fragments with the derived fragments of the dynamic obfuscation factor and storing them on different storage nodes includes: The ciphertext is divided into N ciphertext fragments using a ciphertext threshold secret sharing algorithm. At least K ciphertext fragments are needed to recover the ciphertext, where N>K≥1. The dynamic obfuscation factor is divided into M derivative fragments using a derivative threshold secret sharing algorithm, wherein at least L derivative fragments are required to reconstruct the dynamic obfuscation factor, M>L≥2, and L≥K; The N ciphertext fragments are associated one-to-one with the M derived fragments to generate N association pairs, where N=M; The N association pairs are stored on N geographically isolated storage nodes, such that the number of derived fragments stored individually on any one of the storage nodes is less than L.

6. The method according to claim 5, characterized in that, The step of reconstructing the dynamic obfuscation factor based on the derived fragments and verifying the integrity of the ciphertext fragments includes: At least L derived fragments are obtained from the storage node, and the dynamic obfuscation factor is reconstructed based on the derived threshold secret sharing algorithm; Obtain at least L ciphertext fragments associated with the at least L derived fragments from the storage node; The integrity check value is calculated for each ciphertext fragment using the reconstructed dynamic obfuscation factor; The calculated integrity check value is compared with the pre-stored check value. If they are all consistent, the ciphertext fragment is confirmed to be complete and correctly associated. The pre-stored check value is calculated and stored in advance based on the dynamic confusion factor for each ciphertext fragment after it is divided into N ciphertext fragments. The identity ciphertext is recovered from the at least L ciphertext fragments based on the ciphertext threshold secret sharing algorithm.

7. The method according to claim 1, characterized in that, The process of obtaining the corresponding decryption key based on the requester's permission level, decrypting the reconstructed identity ciphertext, and restoring the user identity data includes: When the requester's permission level is the data permission level, obtain the data decryption key and use the data decryption key to decrypt all data fields in the identity ciphertext; When the requester's permission level is the attribute permission level, obtain the attribute decryption key and the attribute mapping key, wherein the attribute permission level is associated with a predefined set of attributes, and the attribute decryption key is used to decrypt the ciphertext field corresponding to the attribute set; Based on the attribute mapping key, locate and extract the ciphertext field corresponding to the attribute set from the identity ciphertext; The encrypted field is decrypted using the attribute decryption key to recover the field in the user identity data that corresponds to the attribute set.

8. A user identity data encryption and decryption system based on big data, characterized in that, The system includes: The fingerprint acquisition module is used to acquire the user's identity fingerprint; The blinding processing module is used to perform blinding processing on the identity fingerprint, generate a blinded identity identifier, and simultaneously generate a dynamic obfuscation factor bound to the blinded identity identifier; A key generation module is used to generate an encryption key based on the blinded identity identifier; An encryption module is used to encrypt user identity data using the encryption key to obtain ciphertext identity data; The sharded storage module is used to divide the identity ciphertext into multiple ciphertext shards, and associate the multiple ciphertext shards with the derived shards of the dynamic obfuscation factor and store them on different storage nodes, wherein the derived shards stored on any storage node alone cannot reconstruct the dynamic obfuscation factor; The access control module is used to verify the permissions of the requester in response to data access requests; The data acquisition module is used to acquire the multiple encrypted fragments and their associated derived fragments from the different storage nodes after verification. The reconstruction verification module is used to reconstruct the dynamic obfuscation factor based on the derived fragments, verify the integrity of the ciphertext fragments, and reassemble the identity ciphertext based on the multiple ciphertext fragments. The decryption module is used to obtain the corresponding decryption key based on the requester's permission level, decrypt the reconstructed identity ciphertext, and restore the user identity data.

9. A computer device comprising a memory and a processor, the memory being communicatively connected to the processor, and the memory storing a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the user identity data encryption and decryption method based on big data as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the user identity data encryption and decryption method based on big data as described in any one of claims 1 to 7.

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