Information security data processing method based on biological characteristics and quantum enhancement

Abstract

The invention discloses an information security data processing method based on biological characteristics and quantum enhancement, and belongs to the technical field of network and information security. Comprising the steps of collecting and preprocessing multi-dimensional biological characteristic data of a user; generating a dynamic correlation factor through weighted coupling calculation; a dynamic encryption key is generated through nonlinear coupling in combination with the quantum random number sequence; evaluating the environmental safety of the terminal by using a cognitive environmental credibility verification model; if the environment is credible, configuring a chaotic encryption parameter based on a dynamic key to encrypt target data and generate an integrity check code; and the receiving end generates a key through the same process and completes decryption and integrity verification. According to the method, the dynamic generation and'one-time pad 'of the secret key are realized, the secret key does not need to be stored, and a'biology-quantum-environment' three-in-one safety protection system is constructed by combining biological characteristic uniqueness, quantum randomness and environment credibility verification, so that the anti-cracking capability of data encryption and the adaptability of a terminal environment are effectively improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of network and information security, in particular to an information security data processing method based on biological characteristics and quantum enhancement. BACKGROUND

[0002] In the current network information security field, data encryption protection is mostly dependent on fixed key systems (such as RSA and AES algorithms), and such schemes have three major core defects: firstly, the key needs to be stored in the terminal or server for a long time, which is vulnerable to theft and tampering attacks, and the key management cost is high; secondly, the encryption process is separated from the terminal environment, which cannot identify imitated terminals or malicious environments, resulting in a security vulnerability of "encryption is effective but the terminal is untrusted"; and thirdly, the application of biological characteristics is mostly limited to static verification (such as fingerprint comparison and face recognition), and the biological characteristics are not deeply coupled with the key generation, so the static characteristics can be easily forged and copied, and a strong binding relationship between the user and the key cannot be formed.

[0003] In the prior art, some schemes attempt to combine biological characteristics and keys, but mostly adopt the mode of "static biological characteristics + fixed algorithm", such as generating a fixed key through fingerprint characteristics, which still cannot solve the problems of insufficient key dynamics and weak anti-environment interference capability. At the same time, although quantum random number technology has been used for key generation, it lacks a precise association mechanism with the user's biological characteristics, and it is difficult to realize the individualization and uniqueness control of the key. Therefore, there is an urgent need for an information security data processing method that has "key dynamics, terminal trustworthiness and biological uniqueness", so as to improve the security level of data processing from the root. SUMMARY

[0004] The purpose of the application is to provide an information security data processing method based on biological characteristics and quantum enhancement, which solves the problems of high key storage risk, static application of biological characteristics and separation of encryption and environment.

[0005] To achieve the above purpose, the application provides an information security data processing method based on biological characteristics and quantum enhancement, which comprises the following steps: S1: collecting multi-dimensional biological characteristic data of an authorized terminal user, wherein the multi-dimensional biological characteristic data comprises static physiological characteristics, dynamic behavior characteristics and environment-related characteristics; S2: preprocessing the multi-dimensional biological characteristic data to remove noise interference and complete data quality verification; S3: generating a biological characteristic dynamic correlation factor by weighted coupling calculation based on the preprocessed biological characteristic data, wherein the dynamic correlation factor integrates the uniqueness of static characteristics and the time sequence correlation of dynamic characteristics; S4: obtaining a quantum random number sequence by a quantum random number generator, and performing nonlinear coupling between the dynamic correlation factor and the quantum random number sequence to derive a dynamic encryption key; S5: constructing a cognitive environment trusted verification model, collecting terminal operating environment data and inputting the model, and outputting an environment trusted value to determine whether the terminal environment is safe; S6: if the environment trusted value meets the preset threshold, configuring chaotic encryption parameters based on the dynamic encryption key, performing chaotic encryption processing on the target data, and generating an integrity verification code of the correlation key; S7: the receiving end generates the same dynamic encryption key by repeating steps S1-S5, combines the integrity verification code to complete data decryption and integrity verification, and outputs the original target data.

[0006] Further, in step S1, the static physiological features include fingerprint ridge line spacing and fingerprint node curvature, which are collected by a fingerprint pressure sensor with a sampling rate of ≥100Hz; the dynamic behavior features include finger pressing force variation sequence, heart rate fluctuation sequence, and terminal holding angle variation sequence, which are collected by a pressure sensor, an optical heart rate sensor, and a three-axis gyroscope, respectively, with a sampling period of 3-5 seconds; and the environment correlation features include ambient light intensity and terminal device temperature, which are obtained in real time by an ambient light sensor and a terminal system.

[0007] Further, in step S2, the preprocessing includes adaptive Kalman filter denoising and data quality verification, wherein the state transition matrix of the adaptive Kalman filter is According to the feature type dynamic adjustment: the static physiological features correspond to with a value of 0.90-0.95, the dynamic behavior features correspond to with a value of 0.80-0.85, and the environment correlation features correspond to with a value of 0.75-0.80. Data quality verification is realized by a quality score formula, which is . The data is determined to be qualified in time, otherwise re-collection is triggered, wherein, is the static feature definition score, is the dynamic sequence continuity score, is the environment data rationality score.

[0008] Further, in step S3, the calculation process of the dynamic correlation factor includes: first, mapping the preprocessed biological feature data to the [0, 1] interval by a standardization formula, which is . wherein, is the standardized feature value, These are the original eigenvalues. , The statistical extreme value of this feature dimension is used; then the dynamic correlation factor is calculated using the weighted coupling formula. The weighted coupling formula is as follows: ,in For static feature weights, For dynamic feature time series weights, For environmental feature weights, These are the standardized static, dynamic, and environmental characteristic values, respectively. This represents the XOR operation.

[0009] Furthermore, in step S4, the quantum random number generator generates a binary quantum random number sequence based on the photon polarization state measurement principle. , The key length, which can be 128 or 256 bits, and the entropy of the sequence. The nonlinear coupling is achieved through a key derivation formula, which is as follows: ; in, For the first dynamic encryption key Bit, As a dynamic correlation factor, It is a nonlinear adjustment function. This is used for modulo operations to output binary bits.

[0010] Furthermore, in step S5, the cognitive environment trust verification model is constructed using a Long Short-Term Memory (LSTM) network. The input terminal operating environment data includes a process list, network connection status, CPU load, and memory usage. The model outputs an environment trust value. The value range is 0-1, and the preset threshold is 0.8. The core calculation formula of the model is used to determine the security of the terminal environment. ; in, It is the Sigmoid activation function. This is the model weight matrix. This is the environmental feature vector after time-series processing. This is the model bias term.

[0011] Furthermore, in step S6, the chaotic encryption employs a Logistic chaotic system, whose iterative formula is: ; in, For chaotic sequence values, To control parameters, adjust based on environmental confidence values: when hour ,when hour ; The specific process of chaotic encryption involves mapping the chaotic sequence to an integer sequence, performing an XOR operation with the target data in segments, and using the following encryption formula: ; in, To fragment the encrypted data, To shard the raw data, This is the floor function.

[0012] Further, in step S6, the integrity check code is calculated using the CRC of the associated key, with the formula as follows: ,in, This is the standard CRC32 check function. XOR represents the exclusive OR accumulation operation, and K_i is the i-th bit of the dynamic encryption key.

[0013] Furthermore, in step S7, the receiving end must be an authorized terminal, and the biometric data collection target for generating the dynamic encryption key must be consistent with that of the sending end. The decryption process is the inverse operation of the encryption process. Restore the original data, and then compare the received and calculated data. The value verifies the integrity of the data. If the data matches, the data is output; otherwise, a data anomaly warning is triggered.

[0014] Furthermore, the dynamic encryption key adopts a "one-time key" mechanism. After each data processing is completed, the key information in the terminal memory is cleared immediately. The next time it is processed, steps S1-S4 are repeated to regenerate the key. When the terminal detects a change in the biometric data collection object or a sudden change in environmental characteristics, the key regeneration is forcibly triggered.

[0015] Therefore, the information security data processing method based on biometrics and quantum enhancement using the above structure of the present invention has the following beneficial effects: (1) This invention generates a dynamic encryption key in real time based on biometrics and quantum random numbers, stores it only temporarily in memory, and deletes it immediately after processing. This fundamentally avoids the risk of storage theft associated with traditional fixed keys, and the key duplication probability is lower than that of traditional fixed keys. ; (2) This invention integrates static biometrics and dynamic behavioral features into a correlation factor. The difference rate of the feature factor under different states of the same user is ≥15%, which solves the problem that static biometrics are easy to forge and improves the uniqueness of key root data. (3) This invention achieves the linkage between encryption and environment through a cognitive environment verification model. The accuracy rate of environment trust verification is ≥99.2%, which can effectively block data processing under counterfeit terminals and malicious processes and improve the defense capability against unknown attacks. (4) This invention combines the true randomness of quantum random numbers with the nonlinear characteristics of chaotic encryption, and the encrypted data has a brute-force resistance time exceeding [a certain value]. In 2019, it was far superior to the existing AES-256 algorithm. It can withstand quantum computing attacks for years; (5) In this invention, the encryption / decryption time for 1GB of data is ≤30 seconds, which meets the needs of enterprise-level data processing and can be adapted to highly sensitive data security processing scenarios in multiple fields such as finance, medical care, and government affairs.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] Figure 1 This is an overall flowchart of the present invention; Figure 2 Logic diagram for calculating dynamic correlation factors of biological characteristics; Figure 3 This is a schematic diagram of the quantum-enhanced dynamic key derivation principle. Figure 4 This is a schematic diagram illustrating the linkage between environmental verification and chaotic encryption. Detailed Implementation

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] Example The implementation of this invention relies on a sensor kit and a quantum random number generation module of an authorized terminal, specifically configured as follows: Biometric data acquisition module: fingerprint pressure sensor (model: FPC1020, sampling rate 120Hz, pressure accuracy 0.1N), photoelectric heart rate sensor (model: MAX30102, sampling rate 50Hz), three-axis gyroscope (model: MPU6050, sampling rate 200Hz). Environmental acquisition module: Ambient light sensor (model: BH1750, sampling range 0-65535 lux); Quantum random number module: Miniature photon polarization state quantum random number generator (output rate 1Mbps, entropy value 0.9995). Processing module: Quad-core ARM Cortex-A73 processor, ≥4GB memory, ensuring real-time data processing and model calculation.

[0021] like Figures 1-4 As shown, this invention provides an information security data processing method based on biometrics and quantum enhancement, specifically including the following steps: Step S1: Multi-dimensional biometric data collection When an authorized user performs a "fingerprint press + grip" operation on the device, the device simultaneously activates all sensors to collect data for 5 seconds. S11. The fingerprint pressure sensor acquires a fingerprint image and extracts the ridge spacing of 20 feature points. (unit μm) and nodal curvature ( (unit: rad) forms static characteristics ; S12, Pressure sensor collects changes in pressure intensity ( (Unit: N), heart rate sensor collects heart rate fluctuations ( (Unit: times / minute), gyroscope collects changes in grip angle ( (unit: °), forming dynamic characteristics ; S13, Ambient light sensor collects light intensity (Unit: lux) Temperature read by the terminal system (Unit: °C), forming environmental characteristics Collected data is synchronously timestamped (accurate to milliseconds).

[0022] Step S2: Data Preprocessing S21, Adaptive Kalman Filter: For Configure state transition matrices respectively Control matrix Control input Through the filtering formula Noise removal, where Kalman gain The signal-to-noise ratio of the filtered data is ensured to be ≥40dB by real-time calculation of the covariance matrix. S22, Quality Verification: Calculation Value, if ,satisfy If required, proceed to the next step; if If the terminal vibrates, it will prompt "Please press your fingerprint again" until qualified data is collected.

[0023] Step S3: Calculation of dynamic correlation factors S31. Feature Standardization: Based on fingerprint ridge spacing For example, its , ,pass Complete standardization; S32, Weighted Coupling: Configuration The sum is 0.6 (ridge spacing 0.3, node curvature 0.3). The sum is 0.3 (increasing with the sampling order). Substituting into the formula, we get (Retain 6 decimal places) This factor is dynamically adjusted according to the user's pressure level.

[0024] Step S4: Dynamic Key Derivation S41. Quantum Random Number Generation: The quantum module generates a 128-bit sequence. (Entropy value 0.9996); S42, Nonlinear Coupling: [This appears to be a fragment of a sentence and requires more context for accurate translation.] Substituting into the key derivation formula, a 128-bit dynamic key is calculated. The key and and Strong correlation, cannot be reversed using a single parameter.

[0025] Step S5: Environmental Trust Verification S51. Environmental data acquisition: Obtain the terminal process list (no abnormal processes), network connection (only connected to the authorized server), CPU load 30%, memory usage 45%, forming C=[1,1,0.3,0.45]; S52 and LSTM model calculations: via formulas Calculated If the threshold requirement of ≥0.8 is met, the environment is deemed safe.

[0026] Step S6: Chaotic Encryption and Verification Code Generation S61, Chaos Parameter Configuration: Because ,set up It is obtained by converting the first 32 bits of K. ; S62. Data Encryption: Encrypt the target data Fragmented in 1024-byte blocks, via Generate a chaotic sequence, map it to an integer sequence, and then... Perform an XOR operation to obtain encrypted fragments. ; S63. Check code generation: Calculate each... The CRC32 value, combined with pass Generate a 32-bit checksum.

[0027] Step S7: Decryption and Integrity Verification S71. Receiver Key Generation: Authorized receiver users press their fingerprints, and S1-S5 are repeated to generate the same key. ; S72, Decryption Operation: Through reduction ; S73. Integrity Verification: Calculate the received data. With the sent If consistent, confirm that the data has not been tampered with, output the original data; if and If there is a discrepancy, the message "Data error, usage has been terminated" will be displayed.

[0028] Key update mechanism verification: When the user presses their fingerprint a second time, the pressure increases, and the dynamic association factor is updated. The key is changed to 6.123456 and then combined with a new quantum random number sequence to generate a completely new key. , compared to the previous time The difference rate is 92%, achieving "one-time password"; when the terminal is brought to a strong light environment ( Sudden changes in environmental characteristics can cause significant changes in the F value, triggering forced key regeneration and ensuring data processing is interrupted when the environment is insecure.

[0029] Therefore, the present invention employs the above-mentioned information security data processing method based on biometrics and quantum enhancement to achieve the following objectives: 1) The key does not need to be stored; it is dynamically generated based on biometrics and the environment. 2) Biometrics have been upgraded from static verification to dynamic key root data, improving non-replicability; 3) The encryption process is linked with the trusted verification of the terminal environment to block data processing in a malicious environment; 4) Integrating quantum random numbers to enhance key anti-cracking capabilities, forming a three-in-one security protection system of "biology-quantum-environment".

[0030] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for information security data processing based on biometrics and quantum enhancement, characterized in that, Includes the following steps: S1: Collect multi-dimensional biometric data of authorized terminal users, including static physiological characteristics, dynamic behavioral characteristics and environmental characteristics; S2: Preprocess the multi-dimensional biometric data to remove noise interference and complete data quality verification; S3: Based on the preprocessed biometric data, a dynamic correlation factor for biometrics is generated through weighted coupling calculation. The dynamic correlation factor integrates the uniqueness of static features and the temporal correlation of dynamic features. S4: Obtain a quantum random number sequence through a quantum random number generator, and nonlinearly couple the dynamic correlation factor with the quantum random number sequence to derive a dynamic encryption key; S5: Construct a cognitive environment trust verification model, collect terminal operating environment data and input it into the model, and output the environment trust value to determine whether the terminal environment is secure; S6: If the environmental trust value meets the preset threshold, configure the chaotic encryption parameters based on the dynamic encryption key, perform chaotic encryption processing on the target data, and generate an integrity check code for the associated key. S7: The receiving end generates the same dynamic encryption key by repeating steps S1-S5, and completes data decryption and integrity verification by combining the integrity check code, and outputs the original target data.

2. The method according to claim 1, characterized in that, In step S1, the static physiological features include the fingerprint ridge spacing and fingerprint node curvature, which are collected using a fingerprint pressure sensor with a sampling rate of ≥100Hz; the dynamic behavioral features include the finger pressure change sequence, heart rate fluctuation sequence, and terminal grip angle change sequence, with a collection period of 3-5 seconds; the environmental correlation features include ambient light intensity and terminal device temperature.

3. The method according to claim 1, characterized in that, In step S3, the calculation of the dynamic correlation factor includes feature standardization and weighted coupling. The feature standardization is achieved through the formula... accomplish; The weighted coupling is expressed by the formula accomplish; in, For static feature weights, For dynamic feature time series weights, For environmental feature weights, This represents the XOR operation.

4. The method according to claim 1, characterized in that, In step S4, the quantum random number generator generates a binary quantum random number sequence based on the photon polarization state measurement principle. t is 128 or 256 bits, and the entropy value of the sequence. ; The nonlinear coupling is achieved through a key-derived formula. accomplish; in, For the first dynamic encryption key Bit, As a dynamic correlation factor, It is a nonlinear adjustment function.

5. The method according to claim 1, characterized in that, In step S5, the cognitive environment trust verification model is a long short-term memory network model. The input terminal operating environment data includes process list, network connection status, CPU load and memory usage. The environment trust value T(C) output by the model ranges from 0 to 1, and the preset threshold is 0.

8. When T(C) ≥ 0.8, the terminal environment is determined to be secure.

6. The method according to claim 5, characterized in that, The core calculation formula of the cognitive environment trust verification model is: ; in, It is the Sigmoid activation function. , This is the model weight matrix. This is the environmental feature vector after time-series processing. This is the model bias term.

7. The method according to claim 1, characterized in that, In step S6, the chaotic encryption uses a Logistic chaotic system, and its iterative formula is: ; in, For chaotic sequence values, For control parameters, when hour ,when hour .

8. The method according to claim 7, characterized in that, The specific process of chaotic encryption involves mapping the chaotic sequence to an integer sequence, performing an XOR operation with the target data in segments, and using the encryption formula as follows: ; in, To fragment the encrypted data, To shard the raw data, This is the floor function.

9. The method according to claim 1, characterized in that, In step S6, the integrity check code is obtained through the formula generate; in, This is the standard CRC32 check function. This represents the XOR accumulation operation. For the first dynamic encryption key Bit.

10. The method according to claim 1, characterized in that, The dynamic encryption key adopts a "one-time key" mechanism. The key information in the terminal memory is cleared immediately after each data processing. When the terminal detects a change in the biometric data collection object or a sudden change in environmental characteristics, it forcibly triggers key regeneration.

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