Dynamic biological key encryption method and device

Abstract

The invention relates to a dynamic biological key encryption method and device, and belongs to the technical field of information security, and the dynamic biological key encryption method comprises the steps: employing an improved fuzzy extraction algorithm to carry out biological feature code extraction on biological features collected in real time so as to generate a physiological feature base key, performing feature code extraction on the corrected biological features by adopting a fuzzy extraction algorithm, performing entropy source expansion on the biological features by adopting a pseudo-random number generator to obtain a dynamic key fragment, and generating a session temporary key based on the dynamic key fragment and the physiological feature base key; according to the method, the secret key fingerprint is obtained based on the session temporary secret key and the physiological feature base secret key, after the secret key fingerprint is encrypted, the encrypted secret key fingerprint is input into the alliance chain through the smart contract, the block chain anchoring secret key is generated, dynamic biological secret key encryption is completed, and the safety of biological feature encryption is improved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a dynamic biometric key encryption method and device. Background Art

[0002] With the increasingly prosperous development of the big data era, protecting the security of sensitive data information has become crucial. Although traditional encryption methods can provide a certain degree of protection, in the face of increasingly complex security threats, higher-level security measures are needed.

[0003] Traditional biometric encryption methods, such as fingerprints, irises, and faces, are unique and non-replicable. Users do not need to remember complex passwords or carry physical credentials; they can simply provide their biometrics to complete identity verification. Biometrics always accompany individuals, eliminating the need to worry about loss, forgetfulness, or theft, allowing identity verification to be performed anytime, anywhere. However, facing increasingly complex data security threats and biometric leakage threats, the recognition effect may be affected, and the security of traditional biometric encryption may not be guaranteed. Summary of the Invention

[0004] In view of this, it is necessary to provide a dynamic biometric key encryption method and device to solve the technical problem that traditional biometric encryption has low security.

[0005] In order to solve the above problems, in a first aspect, the present invention provides a dynamic biometric key encryption method, comprising: An improved fuzzy extraction algorithm is used to extract the biometric code of the biometric features collected in real time to generate a physiological feature base key, wherein the biometric features are corrected using a BCH code, and the fuzzy extraction algorithm is used to extract the feature code of the corrected biometric features to obtain the physiological feature base key; Using a pseudo-random number generator to perform entropy source expansion on the biometric feature to obtain a dynamic key fragment, and generating a session temporary key based on the dynamic key fragment and a physiological feature base key; A key fingerprint is obtained based on the session temporary key and the physiological feature base key. After encrypting the key fingerprint, the encrypted key fingerprint is input into the alliance chain through the smart contract to generate a blockchain anchor key and complete the dynamic biometric key encryption.

[0006] In a possible implementation, the biometrics include heart rate and brain waves.

[0007] In one possible implementation, the method of extracting a feature code from a biometric feature collected in real time using an improved fuzzy extraction algorithm to generate a physiological feature base key comprises: Obtaining a biometric vector based on biometric features collected in real time; Using BCH code to perform noise-tolerant encoding and stable decoding on the biometric feature vector to obtain a corrected biometric feature; A fuzzy extraction algorithm is used to extract the feature code of the corrected biological feature to obtain a biological feature code, and a hash operation is performed on the biological feature code to obtain a physiological feature base key.

[0008] In one possible implementation, the step of using a pseudo-random number generator to perform entropy source expansion on the biometric feature to obtain a dynamic key fragment includes: Using a pseudo-random number generator to predict the biometric feature to obtain the biometric feature at the next moment; Physical noise data collected by the hardware sensor is used as a random entropy source, and a nonlinear transformation and hash operation are performed on the random entropy source and the biometric feature at the next moment to generate a dynamic key fragment.

[0009] In a possible implementation, generating a temporary session key based on the dynamic key fragment and the physiological feature-based key includes: Performing chaotic mapping on the dynamic key fragment and the physiological feature base key to generate a session temporary key.

[0010] In a possible implementation, the generating of the session temporary key based on the dynamic key fragment and the physiological feature-based key further includes: When the session temporary key is used to verify the biometric feature, the biometric feature collected in real time is matched, wherein a first matching threshold and a second matching threshold are set, the heart rate collected in real time is matched with the heart rate collected last time to obtain a first matching value, the brain wave collected in real time is matched with the brain wave collected last time to obtain a second matching value, and the total matching value is determined based on the first matching value and the second matching value. When the total matching value is greater than or equal to the first matching threshold, the verification is passed, and a new session temporary key is generated according to the biometric feature collected in real time. When the total matching value is less than the first matching threshold and greater than or equal to the second matching threshold, the voice and gesture of the organism are verified. When the total matching value is less than the second matching threshold, the verification fails.

[0011] In a possible implementation, obtaining a key fingerprint based on the session transient key and the physiological feature-based key includes: Performing a hash operation on the session temporary key and the physiological feature base key to obtain a key hash; The operation time and operation record are obtained, and the operation time, operation record, key hash, session temporary key and physiological feature base key are aggregated through a hash algorithm to generate a key fingerprint.

[0012] In one possible implementation, the encrypted key fingerprint is input into the consortium chain through a smart contract to generate a blockchain anchor key, including: Encrypting the key fingerprint using an asymmetric encryption algorithm and an attribute-based encryption algorithm; The encrypted key fingerprint is input into the consortium chain through the smart contract, and the blockchain anchor key is generated based on the on-chain hash value of the key fingerprint and the session temporary key.

[0013] In one possible implementation, the process of inputting the encrypted key fingerprint into the consortium chain through a smart contract to generate a blockchain anchor key also includes: When requesting access to protected data, a biometric two-way authentication phase is added to the TLS1.3 handshake protocol. When the access is completed, the physiological feature base key and the session temporary key are destroyed.

[0014] In a second aspect, the present invention further provides a dynamic biometric key encryption device, comprising: A base key generation module is used to extract the biometric code of the biometric features collected in real time using an improved fuzzy extraction algorithm to generate a physiological feature base key, wherein the biometric features are corrected using a BCH code and the feature code of the corrected biometric features is extracted using a fuzzy extraction algorithm to obtain the physiological feature base key; A temporary key generation module is configured to perform entropy source expansion on the biometric feature using a pseudo-random number generator to obtain a dynamic key fragment, and generate a session temporary key based on the dynamic key fragment and a physiological feature base key; The anchor key generation module is used to obtain a key fingerprint based on the session temporary key and the physiological feature base key, encrypt the key fingerprint, and input the encrypted key fingerprint into the alliance chain through the smart contract to generate a blockchain anchor key and complete dynamic biometric key encryption.

[0015] The beneficial effects of the present invention are: using an improved fuzzy extraction algorithm to extract biometric codes from biometric features collected in real time to generate a physiological feature base key; extracting the biometric code through the improved fuzzy extraction algorithm improves the stability of the key; using a pseudo-random number generator to expand the entropy source of the biometric to obtain a dynamic key fragment; generating a session temporary key based on the dynamic key fragment and the physiological feature base key; generating the dynamic key fragment through a pseudo-random number generator, thereby improving the randomness and security of the key; inputting the encrypted key fingerprint into the alliance chain through a smart contract to generate a blockchain anchor key, thereby ensuring the authenticity and integrity of the data, being able to effectively deal with data tampering, and improving the security of biometric encryption. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For the technical personnel of the present invention, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flow chart of an embodiment of the dynamic biometric key encryption method provided by the present invention; Figure 2 A schematic diagram of key encryption for the dynamic biometric key encryption method provided by the present invention; Figure 3 A schematic diagram of key verification for the dynamic biometric key encryption method provided by the present invention; Figure 4 This is a structural diagram of an embodiment of the dynamic biometric key encryption device provided by the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0019] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0020] Before presenting the embodiments, the following terms are explained.

[0021] Fuzzy extraction algorithm: It is an algorithm that specifically processes fuzzy, incomplete, or noisy data, aiming to efficiently extract key information or features from it. Its core lies in overcoming data uncertainty through mathematical or statistical methods to ensure the robustness and reliability of the extraction results. It is used for biometric recognition and converts noisy data into a stable key through error correction coding.

[0022] A specific embodiment of the present invention discloses a dynamic biometric key encryption method, such as Figure 1 As shown, the dynamic biometric key encryption method includes: S101. Using an improved fuzzy extraction algorithm to extract a biometric code from a biometric feature collected in real time to generate a physiological feature base key, wherein the biometric feature is corrected using a BCH code, and a fuzzy extraction algorithm is used to extract a feature code from the corrected biometric feature to obtain the physiological feature base key; It should be noted that biometric features include heart rate (PPG) and brain waves (EEG). By synchronously collecting heart rate (PPG) and brain waves (EEG), the difficulty of fraudulent use is increased. The biometric feature code is extracted through an improved fuzzy extraction algorithm, which improves the stability of the key.

[0023] S102. Use a pseudo-random number generator to perform entropy source expansion on the biometric feature to obtain a dynamic key fragment, and generate a temporary session key based on the dynamic key fragment and the physiological feature base key; It should be noted that the pseudo-random number generator is LSTM-PRNG, which generates dynamic key fragments, thereby improving the randomness and security of the key.

[0024] S103. Obtain a key fingerprint based on the session temporary key and the physiological feature base key, encrypt the key fingerprint, and input the encrypted key fingerprint into the consortium chain through the smart contract. After generating the blockchain anchor key, dynamic biometric key encryption is completed. It should be noted that the key fingerprint is encrypted by combining AES-256 encryption and CP-ABE attribute encryption to achieve fine-grained access control. The key fingerprint is stored on the consortium chain through smart contracts to generate blockchain-anchored keys and realize key lifecycle management.

[0025] In some embodiments, in step S101, an improved fuzzy extraction algorithm is used to extract a biometric code from the biometric features collected in real time to generate a physiological feature base key, wherein the biometric features are corrected using a BCH code, and the fuzzy extraction algorithm is used to extract a feature code from the corrected biometric features to obtain a physiological feature base key; the biometric features include heart rate (PPG) and brain wave (EEG), and the user's heart rate (PPG) and brain wave (EEG) data are synchronously collected through a biosensor, and a biometric feature vector is obtained based on the biometric features collected in real time, and a threshold mechanism is set to convert the biometric features into a specific value through the threshold mechanism. The heart rate and brain waves of the human body are double-bound to ensure that both the heart rate and brain waves must match within a certain range to generate a key. After binding the heart rate and brain waves, the biometric vector is obtained, and the BCH code is used to perform noise-tolerant encoding and stable decoding on the biometric vector to obtain a corrected biometric code. The BCH code adaptively selects the error correction strength according to the signal quality index (SQI) to adapt to the fluctuation of the biological signal, thereby obtaining the corrected biometric code. The corrected biometric code is hashed to obtain the physiological characteristic base key, which is one of the triple dynamic key architectures.

[0026] In some implementations, in step S102, a pseudo-random number generator is used to perform entropy source expansion on the biometric feature to obtain a dynamic key fragment, specifically: a pseudo-random number generator is used to predict the biometric feature to obtain the biometric feature at the next moment, physical noise data collected by the hardware sensor is used as a random entropy source, and the random entropy source and the biometric feature at the next moment are nonlinearly transformed and hashed to generate a dynamic key fragment; the dynamic key fragment is a 256-bit key fragment; that is, based on real-time physiological data, an LSTM-based pseudo-random number generator (LSTM-PRNG) is used to predict the physiological feature data at the next moment, and the physiological feature data at the next moment is combined with the read hardware sensor noise data to generate a dynamic key fragment; a session temporary key is generated based on the dynamic key fragment and the physiological feature base key, and a chaotic mapping is performed on the dynamic key fragment and the physiological feature base key to generate a session temporary key; when the user accesses the protected data, the user's biometric features (heart rate and brain waves) are verified by the session temporary key at the beginning of each access session, and a dynamic key is generated; the session temporary key is one of the triple dynamic key architecture.

[0027] In some embodiments, in step S103, a key fingerprint is obtained based on the session temporary key and the physiological feature base key, a hash operation is performed on the session temporary key and the physiological feature base key to obtain a key hash, the operation time and operation record are obtained, and the operation time, operation record, key hash, session temporary key and physiological feature base key are aggregated through a hash algorithm to generate a key fingerprint, which consists of the key hash, operation time, operation record, physiological feature base key and session temporary key; after obtaining the key fingerprint, the key fingerprint is encrypted, and the encrypted key fingerprint is input into the alliance chain through a smart contract. After generating the blockchain anchor key, dynamic biometric key encryption is completed, and the key fingerprint is encrypted using an asymmetric encryption algorithm (AES-256) and an attribute-based encryption algorithm (CP-ABE). The encrypted key fingerprint is input into the alliance chain through a smart contract, and a blockchain anchor key is generated based on the on-chain hash value of the key fingerprint and the session temporary key. The blockchain anchor key is one of the triple dynamic key architectures.

[0028] The physiological feature base key, session temporary key, and blockchain anchor key are in a collaborative relationship. The physiological feature base key extracts the biometric code of the dual biometrics (PPG, EEG) through a fuzzy extraction algorithm to generate a stable key. Based on the physiological feature base key and real-time biometrics, a dynamic key fragment is generated through LSTM-PRNG to generate a session temporary key. When a user accesses data, a new dynamic key is generated through the session temporary key. Each time a new dynamic key is generated and an old key is destroyed, it will be recorded in the alliance chain. The key fingerprint is stored on the alliance chain through a smart contract to generate a blockchain anchor key to achieve key life cycle management.

[0029] In key lifecycle management, when a user requests access to protected data, their identity is verified using a biometric base key, a session ephemeral key, and a blockchain-anchored key. Upon successful verification, a new dynamic key is generated using the user's current biometrics, and this key is used to encrypt communications. When accessing protected data, the TLS 1.3 handshake protocol is improved by adding a biometric authentication extension to the TLS 1.3 handshake protocol, adding a two-way biometric authentication phase, in which the client verifies the server's biometric data, and the server verifies the client's biometric data. The biometric entropy generated during each handshake is used only to generate keys for the current session. This entropy is session-specific random data generated by real-time collection of user biometrics (heart rate, brain waves) and integration with hardware sensor noise. The user's biometrics are verified in real time during each session. At the end of each session, the biometric base key and session ephemeral key are destroyed, ensuring that even a key leak does not affect the security of other sessions. All key usage records are permanently stored on the blockchain, ensuring traceability of the key lifecycle. Each time a key is generated, destroyed, or rotated, the corresponding transaction ID and timestamp are recorded.

[0030] For a diagram of key encryption, see Figure 2 ,like Figure 2 As shown, after collecting the patient's real-time heart rate and brain waves, a temporary key is generated, which includes a physiological feature base key and a session temporary key. A secure channel for the hospital data center is established by improving the TLS1.3 protocol. The patient's CT image data and the temporary key are mixed and encrypted using the AES-256 and CP-ABE encryption algorithms. Then, the encrypted records and the key fingerprint generated by the CT image data and the temporary key are written into the medical alliance chain through a smart contract in the secure channel of the hospital data center, thereby generating a blockchain anchor key.

[0031] For key verification diagram, see Figure 3 ,like Figure 3 As shown in the figure, when the session temporary key verifies the biometrics, the wearable device collects the patient's biometric signals (heart rate and brain waves) in real time. After filtering and noise reduction, the matching degree is calculated based on the previous biometric signal and the current biometric signal, and the judgment is made based on the set threshold. The threshold is: , , set the first matching threshold, the first matching threshold is 0.8, set the second matching threshold, the second matching threshold is 0.7, match the real-time collected heart rate with the last collected heart rate to obtain the first matching value, match the real-time collected brain waves with the last collected brain waves to obtain the second matching value, and determine the total matching value based on the first matching value and the second matching value. When the total matching value of the heart rate and brain waves is greater than or equal to the first matching threshold of 0.8, the verification is passed and the data can be accessed. A new temporary session key is generated based on the current biometrics, and the old temporary session key is destroyed and entered into the medical alliance chain; when the total matching value is less than the first matching threshold of 0.8 and greater than or equal to the second matching value of 0.7, a secondary verification is performed to verify the voice or gesture. After passing, the data can be accessed. When the total matching score is less than the second matching value of 0.7, the abnormality is recorded in the medical alliance chain and the account is frozen.

[0032] In summary, the dynamic biometric key encryption method provided by the present invention uses an improved fuzzy extraction algorithm to extract the biometric code of the biometric features collected in real time to generate a physiological feature base key, wherein the BCH code is used to correct the biometric features, and the fuzzy extraction algorithm is used to extract the feature code of the corrected biometric features to obtain the physiological feature base key, and a pseudo-random number generator is used to expand the entropy source of the biometric features to obtain a dynamic key fragment, and a session temporary key is generated based on the dynamic key fragment and the physiological feature base key; a key fingerprint is obtained based on the session temporary key and the physiological feature base key, and after encrypting the key fingerprint, the encrypted key fingerprint is input into the alliance chain through the smart contract to generate a blockchain anchor key, thereby completing the dynamic biometric key encryption and improving the security of the biometric encryption.

[0033] In order to better implement the dynamic biometric key encryption method in the embodiment of the present invention, based on the dynamic biometric key encryption method, correspondingly, Figure 4 As shown, an embodiment of the present invention further provides a dynamic biometric key encryption device, the dynamic biometric key encryption device 400 comprising: Base key generation module 401 is configured to extract a biometric code from a biometric feature collected in real time using an improved fuzzy extraction algorithm to generate a physiological feature base key. The biometric feature is corrected using a BCH code, and a fuzzy extraction algorithm is used to extract a feature code from the corrected biometric feature to obtain the physiological feature base key. Temporary key generation module 402, configured to perform entropy source expansion on the biometric feature using a pseudo-random number generator to obtain a dynamic key fragment, and generate a session temporary key based on the dynamic key fragment and the physiological feature base key; The anchor key generation module 403 is used to obtain a key fingerprint based on the session temporary key and the physiological feature base key, encrypt the key fingerprint, and input the encrypted key fingerprint into the alliance chain through the smart contract to generate a blockchain anchor key and complete dynamic biometric key encryption.

[0034] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or replacements that can be easily conceived by any technician familiar with the technical neighbors within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A dynamic biometric key encryption method, characterized in that: include: An improved fuzzy extraction algorithm is used to extract the feature code of the biometric features collected in real time to generate a physiological feature base key, wherein the biometric features are corrected using a BCH code, and the fuzzy extraction algorithm is used to extract the feature code of the corrected biometric features to obtain the physiological feature base key; Using a pseudo-random number generator to perform entropy source expansion on the biometric feature to obtain a dynamic key fragment, and generating a session temporary key based on the dynamic key fragment and a physiological feature base key; A key fingerprint is obtained based on the session temporary key and the physiological feature base key. After encrypting the key fingerprint, the encrypted key fingerprint is input into the alliance chain through the smart contract to generate a blockchain anchor key and complete the dynamic biometric key encryption.

2. The dynamic biometric key encryption method according to claim 1, characterized in that: The biometrics include heart rate and brain waves.

3. The dynamic biometric key encryption method according to claim 2, characterized in that: The improved fuzzy extraction algorithm is used to extract the feature code of the biometric feature collected in real time to generate the physiological feature base key, wherein the biometric feature is corrected using the BCH code and then the fuzzy extraction algorithm is used to extract the feature code of the corrected biometric feature to obtain the physiological feature base key, including: Obtaining a biometric vector based on biometric features collected in real time; Using BCH code to perform noise-tolerant encoding and stable decoding on the biometric feature vector to obtain a corrected biometric feature; A fuzzy extraction algorithm is used to extract the feature code of the corrected biological feature to obtain a biological feature code, and a hash operation is performed on the biological feature code to obtain a physiological feature base key.

4. The dynamic biometric key encryption method according to claim 2, characterized in that: The step of using a pseudo-random number generator to perform entropy source expansion on the biometric feature to obtain a dynamic key fragment includes: Using a pseudo-random number generator to predict the biometric feature to obtain the biometric feature at the next moment; Physical noise data collected by the hardware sensor is used as a random entropy source, and a nonlinear transformation and hash operation are performed on the random entropy source and the biometric feature at the next moment to generate a dynamic key fragment.

5. The dynamic biometric key encryption method according to claim 4, characterized in that: The generating of a session temporary key based on the dynamic key fragment and the physiological feature-based key includes: Performing chaotic mapping on the dynamic key fragment and the physiological feature base key to generate a session temporary key.

6. The dynamic biometric key encryption method according to claim 5, characterized in that: The step of generating a temporary session key based on the dynamic key fragment and the physiological feature-based key further includes: When the session temporary key is used to verify the biometric feature, the biometric feature collected in real time is matched, wherein a first matching threshold and a second matching threshold are set, the heart rate collected in real time is matched with the heart rate collected last time to obtain a first matching value, the brain wave collected in real time is matched with the brain wave collected last time to obtain a second matching value, and the total matching value is determined based on the first matching value and the second matching value. When the total matching value is greater than or equal to the first matching threshold, the verification is passed, and a new session temporary key is generated according to the biometric feature collected in real time. When the total matching value is less than the first matching threshold and greater than or equal to the second matching threshold, the voice and gesture of the organism are verified. When the total matching value is less than the second matching threshold, the verification fails.

7. The dynamic biometric key encryption method according to claim 5, characterized in that: The obtaining of a key fingerprint based on the session temporary key and the physiological feature base key includes: Performing a hash operation on the session temporary key and the physiological feature base key to obtain a key hash; The operation time and operation record are obtained, and the operation time, operation record, key hash, session temporary key and physiological feature base key are aggregated through a hash algorithm to generate a key fingerprint.

8. The dynamic biometric key encryption method according to claim 7, characterized in that: The encrypted key fingerprint is input into the consortium chain through the smart contract to generate the blockchain anchor key, including: Encrypting the key fingerprint using an asymmetric encryption algorithm and an attribute-based encryption algorithm; The encrypted key fingerprint is input into the consortium chain through the smart contract, and the blockchain anchor key is generated based on the on-chain hash value of the key fingerprint and the session temporary key.

9. The dynamic biometric key encryption method according to claim 8, characterized in that: The method of inputting the encrypted key fingerprint into the consortium chain through the smart contract to generate the blockchain anchor key also includes: When requesting access to protected data, a biometric two-way authentication phase is added to the TLS1.3 handshake protocol. When the access is completed, the physiological feature base key and the session temporary key are destroyed.

10. A dynamic biometric key encryption device, characterized in that: include: A base key generation module is used to extract the biometric code of the biometric features collected in real time using an improved fuzzy extraction algorithm to generate a physiological feature base key, wherein the biometric features are corrected using a BCH code and the feature code of the corrected biometric features is extracted using a fuzzy extraction algorithm to obtain the physiological feature base key; A temporary key generation module is configured to perform entropy source expansion on the biometric feature using a pseudo-random number generator to obtain a dynamic key fragment, and generate a session temporary key based on the dynamic key fragment and a physiological feature base key; The anchor key generation module is used to obtain a key fingerprint based on the session temporary key and the physiological feature base key, encrypt the key fingerprint, and input the encrypted key fingerprint into the alliance chain through the smart contract to generate a blockchain anchor key and complete dynamic biometric key encryption.

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