CN117971165B - A method and device for generating pseudo-random numbers - Google Patents

Abstract

The present invention relates to the field of random number generators, and in particular to a method and device for generating pseudo-random numbers, the method comprising: constructing an entropy pool, using the entropy pool to collect entropy source data from a system environment, and performing layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data, thereby generating a first initial seed; using a terminal storage module to collect a variety of random data inside a terminal device, performing interception, shifting and mixing operations on the various random data to generate a second initial seed; generating third data based on the first initial seed and the second initial seed, performing a second step of hash processing on the third data using a second hash algorithm, thereby obtaining a final pseudo-random number seed; inputting the final pseudo-random number seed into a pseudo-random number generator to achieve the generation of pseudo-random numbers. The present invention can achieve the generation of random number seeds that meet standards, are unpredictable, collision-resistant and publicly verifiable.

Description

A method and device for generating pseudo-random numbers

Technical Field

The present invention relates to the technical field of random number generators, and in particular to a method and device for generating pseudo-random numbers.

Background technique

Random numbers play an important role in all walks of life, with rich application scenarios and broad practical significance. For example, in the fields of science and engineering, random numbers are widely used to simulate and model natural phenomena and random events; in the financial field, random numbers are applied to financial models and risk assessments to simulate market fluctuations, calculate option pricing, and conduct risk analysis; in computer graphics, random numbers are also used to create realistic effects of natural phenomena (such as flames, water ripples, etc.); the main and most important application scenario is in the field of cryptography and security, where random numbers are a key element of data protection. They are used to generate secure passwords, encryption keys, and initialization vectors to ensure the confidentiality of sensitive information. Keys that lack randomness may be easily cracked, so secure random number generation is essential for network security.

The seed is input into the random number generator as the initial value to initialize the random number generation algorithm, which is a key parameter for generating high-quality random numbers. A common method is to use the current system time as the seed of the random number generator. Since time is usually constantly changing, this method can provide relatively good randomness. However, in some cases, if multiple programs are started at the same time, the same time may be used as the seed, resulting in the generation of the same random number sequence. This makes it easy for malicious attackers to predict the random numbers generated at certain points in time through this vulnerability, thereby threatening private information such as keys generated by the random numbers, resulting in insecurity. Another method is to generate seeds by processing the initially generated seed data with a single hash algorithm or performing XOR processing on the internal information interception combination. For example, the patent (CN202211123608.4) discloses a random number seed generation method, device, electronic device and storage medium, which discloses a random number seed generation method: obtaining the user input data as the first array, combining the second array and at least one item in the third array to determine the first string; then performing N+1 hash iterations on the first string to generate a first random number seed; wherein the input of the first hash iteration calculation on the first string is the first string, or the input of the i-th hash iteration calculation on the first string is composed of the output of the i-1-th hash iteration calculation on the first string and the second array; performing a hash operation on the second string to generate a random number. This combination of the initial seed data with a deterministic operation (such as hash processing or intercepting internal information) may reduce the randomness of the generated seed. Due to the rapid increase in computer computing power, the seed data undergoes a single hash processing or splicing operation, and attackers may try to analyze the processing process to predict the generated random number. If the processing process is not complex or random enough, the attacker may successfully predict part or all of the random number, which will also lead to insecurity.

Summary of the invention

In order to solve the technical problem that the generated seeds in the prior art are prone to reduce randomness and lack of security, the embodiment of the present invention provides a pseudo-random number generation method and device. The technical solution is as follows:

On the one hand, a pseudo-random number generation method is provided, the method is implemented by a pseudo-random number generation device, and the method includes:

S1. Construct an entropy pool, use the entropy pool to collect entropy source data from the system environment, and perform layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data, thereby generating a first initial seed.

S2. Use the terminal storage module to collect various random data inside the terminal device, perform interception, shift and mixing operations on the various random data, and generate a second initial seed.

S3. Generate third data according to the first initial seed and the second initial seed, and use the second hash algorithm to perform a second hash process on the third data to obtain a final pseudo-random number seed.

S4. Input the final pseudo-random number seed into the pseudo-random number generator to realize the generation of pseudo-random numbers.

Optionally, the entropy pool in S1 includes: a mixed entropy source data storage module, a first-layer chaos algorithm mapping module, a second-layer chaos algorithm mapping module, and a processed data storage module.

Among them, the mixed entropy source data storage module is used to store the entropy source data collected from the system environment, and mix the entropy source data to obtain mixed multivariate data.

The first-layer chaos algorithm mapping module is used to use the first chaos algorithm to perform the first-layer mapping on the mixed multivariate data to obtain the first-layer mapped data.

The second layer chaos algorithm mapping module is used to use the second chaos algorithm to perform the second layer mapping on the data after the first layer mapping to obtain the mixed entropy source data.

The processed data storage module is used to store the mixed entropy source data.

Optionally, the first chaos algorithm is Henon chaos algorithm.

The second chaos algorithm is the Lorenz chaos algorithm.

Optionally, the entropy source data is collected from the system environment by using the entropy pool in S1, and the entropy source data is mixed and mapped layer by layer to obtain mixed entropy source data, and then the first initial seed is generated, including:

S11. Collect entropy source data from the system environment using an entropy pool, and perform layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data.

S12, perform an entropy estimation test on the mixed entropy source data to obtain the entropy source data after the test, and determine whether the entropy source data after the test meets the randomness requirements. If yes, execute step S13; if not, go to execute step S11.

S13. Use a first hash algorithm to perform a first step hash processing on the entropy source data that meets the randomness requirements to generate a first initial seed.

Optionally, the step of collecting a plurality of random data in the terminal device by using the terminal storage module in S2, performing interception, shifting and mixing operations on the plurality of random data to generate a second initial seed includes:

S21. Use the terminal storage module to collect random data of a preset byte length inside the terminal device, and randomly intercept unique identification information of the terminal device according to the preset byte length.

S22, performing a truncation, shift and mixing operation on the random data and the unique identification information to generate a second initial seed.

Optionally, generating third data according to the first initial seed and the second initial seed in S3, performing a second hash process on the third data using a second hash algorithm, and thereby obtaining a final pseudo-random number seed, includes:

S31, performing random splitting processing on the first initial seed and the second initial seed respectively, performing shift-addition operation on equal-length information segments obtained by the random splitting processing, and generating third data.

S32. Perform a second hash process on the third data using a second hash algorithm to obtain a new data column, and output a random number seed according to the new data column and a preset number of output bits.

S33, verify the validity of the random number seed. If it passes the validity verification, the final pseudo-random number seed is obtained; if it fails the validity verification, go to step S31.

On the other hand, a pseudo-random number generating device is provided, which is applied to a pseudo-random number generating method, and the device comprises:

The first generation module is used to build an entropy pool, use the entropy pool to collect entropy source data from the system environment, and perform layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data, thereby generating a first initial seed.

The second generating module is used to collect various random data inside the terminal device by using the terminal storage module, perform interception, shift and mixing operations on the various random data, and generate a second initial seed.

The seed generation module is used to generate third data according to the first initial seed and the second initial seed, and perform a second hash process on the third data using a second hash algorithm to obtain a final pseudo-random number seed.

The output module is used to input the final pseudo-random number seed into the pseudo-random number generator to realize the generation of pseudo-random numbers.

Optionally, the entropy pool includes: a mixed entropy source data storage module, a first-layer chaos algorithm mapping module, a second-layer chaos algorithm mapping module, and a processed data storage module.

Among them, the mixed entropy source data storage module is used to store the entropy source data collected from the system environment, and mix the entropy source data to obtain mixed multivariate data.

The first-layer chaos algorithm mapping module is used to use the first chaos algorithm to perform the first-layer mapping on the mixed multivariate data to obtain the first-layer mapped data.

The second layer chaos algorithm mapping module is used to use the second chaos algorithm to perform the second layer mapping on the data after the first layer mapping to obtain the mixed entropy source data.

The processed data storage module is used to store the mixed entropy source data.

Optionally, the first chaos algorithm is Henon chaos algorithm.

The second chaos algorithm is the Lorenz chaos algorithm.

Optionally, the first generating module is further used to:

S11. Collect entropy source data from the system environment using an entropy pool, and perform layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data.

S12, perform an entropy estimation test on the mixed entropy source data to obtain the entropy source data after the test, and determine whether the entropy source data after the test meets the randomness requirements. If yes, execute step S13; if not, go to execute step S11.

S13. Use a first hash algorithm to perform a first step hash processing on the entropy source data that meets the randomness requirements to generate a first initial seed.

Optionally, the second generating module is further used to:

S21. Use the terminal storage module to collect random data of a preset byte length inside the terminal device, and randomly intercept unique identification information of the terminal device according to the preset byte length.

S22, performing a truncation, shift and mixing operation on the random data and the unique identification information to generate a second initial seed.

Optionally, the seed generation module is further used to:

S31, performing random splitting processing on the first initial seed and the second initial seed respectively, performing shift-addition operation on equal-length information segments obtained by the random splitting processing, and generating third data.

S32. Perform a second hash process on the third data using a second hash algorithm to obtain a new data column, and output a random number seed according to the new data column and a preset number of output bits.

S33, verify the validity of the random number seed. If it passes the validity verification, the final pseudo-random number seed is obtained; if it fails the validity verification, go to step S31.

On the other hand, a pseudo-random number generating device is provided, comprising: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the pseudo-random number generating methods described above is implemented.

On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned pseudo-random number generation methods.

The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

In the embodiment of the present invention, multiple high-quality entropy source data of the system environment are collected, and a mature chaos algorithm is used to mix the data to generate a random seed, thereby ensuring the randomness of the random seed generation and the unpredictability of the random number seed generation.

Two mature domestic and foreign HASH algorithms are adopted to ensure the collision resistance of the generated random seeds.

A method of combining two types of random information in a unique environment of a terminal device to generate a preliminary seed is adopted, thereby improving the security of the random number seed.

The use of minimum entropy estimation method and seed validity verification module ensures that the generated random number seeds meet domestic standards and ensures the security of random number seeds.

And the random number seed can be publicly verified intuitively through the seed validity verification results.

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIG1 is a flow chart of a pseudo-random number generation method provided by an embodiment of the present invention;

2 is a schematic diagram showing the principle of a method for performing splitting and shift-adding operations on two pieces of information, an initial seed A and an initial seed B, provided by an embodiment of the present invention;

FIG3 is a schematic diagram of a flow chart of a random number seed generation method provided by an embodiment of the present invention;

FIG4 is a schematic diagram of the structure of an entropy pool module provided in an embodiment of the present invention;

FIG5 is a schematic diagram of a process for generating an initial seed A provided by an embodiment of the present invention;

FIG6 is a schematic diagram of a process for generating an initial seed B provided by an embodiment of the present invention;

FIG7 is a schematic flow chart of generating a final seed D provided by an embodiment of the present invention;

FIG8 is a block diagram of a pseudo-random number generating device provided by an embodiment of the present invention;

FIG9 is a schematic diagram of the structure of a random number seed generating method provided by an embodiment of the present invention;

FIG10 is a schematic diagram of the structure of a pseudo-random number generating device provided in an embodiment of the present invention.

Detailed ways

The technical solution of the present invention is described below in conjunction with the accompanying drawings.

In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and/or" can be both, or it can be either of the two.

In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

In the embodiments of the present invention, sometimes a subscript such as _W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

The embodiment of the present invention provides a pseudo-random number generation method, which can be implemented by a pseudo-random number generation device, which can be a terminal or a server. As shown in the pseudo-random number generation method flow chart of Figure 1, the processing flow of the method may include the following steps:

S1. Construct an entropy pool, use the entropy pool to collect entropy source data from the system environment, and perform layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data, thereby generating a first initial seed.

Optionally, create an entropy pool of a specific structure as an entropy source data collector to collect random information from multiple entropy sources generated in the system environment through an interface. According to domestic standard requirements, the entropy pool size should be greater than or equal to 512 bytes, but not more than 4096 bytes.

Specifically, the entropy pool may include: a mixed entropy source data storage module, a first-layer chaos algorithm mapping module, a second-layer chaos algorithm mapping module, and a processed data storage module.

Among them, the mixed entropy source data storage module is used to store the entropy source data collected from the system environment, and mix the entropy source data to obtain mixed multivariate data.

The first-layer chaos algorithm mapping module is used to use the first chaos algorithm to perform the first-layer mapping on the mixed multivariate data to obtain the first-layer mapped data.

Among them, the first chaos algorithm can be the Henon chaos algorithm.

The second layer chaos algorithm mapping module is used to use the second chaos algorithm to perform the second layer mapping on the data after the first layer mapping to obtain the mixed entropy source data.

The second chaos algorithm may be a Lorenz chaos algorithm.

The processed data storage module is used to store the mixed entropy source data.

Optionally, the step of collecting entropy source data from the system environment by using the entropy pool in S1, and performing layer-by-layer hybrid mapping on the entropy source data to obtain hybrid entropy source data, and then generating the first initial seed, may include the following steps S11-S13:

S11. Collect entropy source data from the system environment using an entropy pool, and perform layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data.

In a feasible implementation manner, the system may be a financial terminal system or the like.

Furthermore, the entropy pool is used to collect random information from various entropy sources generated in the system environment, such as system time, mouse movement, keyboard keystrokes, disk activity, etc.; two chaotic algorithms are used to perform layer-by-layer mixed mapping on the above entropy source data to increase its randomness, and then the processed data is returned to a specific storage location in the entropy pool.

S12, perform an entropy estimation test on the mixed entropy source data to obtain the entropy source data after the test, and determine whether the entropy source data after the test meets the randomness requirements. If yes, execute step S13; if not, go to execute step S11.

In a feasible implementation, the entropy source data after the chaotic algorithm mapping and mixing is transmitted to the entropy estimation module for entropy estimation test.

Specifically, the most commonly used entropy measurement method is Shannon entropy, which is widely used in fields such as information theory. Shannon entropy refers to the average entropy value of the entropy source. In the field of cryptography, minimum entropy is more commonly used. Minimum entropy is the lower bound of the entropy value of the entropy source, and has higher security requirements. Therefore, the entropy estimation method used in the entropy estimation module set here is minimum entropy, and the calculation formula is shown in the following formula (1):

(1)

Assume that the sequence output by an entropy source has the minimum entropy , then the maximum probability that the attacker guesses the sequence is/> , if the attacker has/> guessing opportunities, then the maximum probability that he guesses correctly is/> .

The random number generator is designed to provide the maximum possible output probability for the entropy source Estimates , and then get the minimum entropy estimate/> .

Furthermore, test whether the collected entropy source data meets the randomness requirements; add an entropy pool data update module. If the entropy estimation test does not meet the requirements, use the entropy pool to collect the entropy source data again, update the internal data of the entropy pool (multiple entropy source data) and continue processing according to the above processing method (chaotic algorithm mixed data); if it meets the requirements, proceed to the next step.

S13. Use a first hash algorithm to perform a first step hash processing on the entropy source data that meets the randomness requirements to generate a first initial seed.

In a feasible implementation, a first HASH cryptographic algorithm (the HASH cryptographic algorithm meets certain security requirements) is used to perform the first step of hash calculation on the mapped data, and the initial seed A is generated according to the required number of bits.

S2. Use the terminal storage module to collect various random data inside the terminal device, perform interception, shift and mixing operations on the various random data, and generate a second initial seed.

The terminal storage module may be a RAM.

Optionally, the above step S2 may include the following steps S21-S22:

S21. Use the terminal storage module to collect random data of a preset byte length inside the terminal device, and randomly intercept unique identification information of the terminal device according to the preset byte length.

S22, performing a truncation, shift and mixing operation on the random data and the unique identification information to generate a second initial seed.

In a feasible implementation, two types of random information are collected in a unique environment of a terminal device. Specifically, random data of a byte length set by an NVRAM (Non-Volatile Random Access Memory) in the terminal device is collected through an internal authorization interface, and unique identification information of the terminal device is randomly intercepted according to the set byte length; the two different pieces of collected information are stored in a memory and shifted and mixed, and the required length of the mixed information column is randomly intercepted as an initial seed B.

Furthermore, the process includes a storage update function, which detects the storage module, pre-sets the random information collected by the storage module to be updated at a required time, and immediately updates the random information in the storage module if it is detected that the random information in the storage module has been used once.

S3. Generate third data based on the first initial seed and the second initial seed for generating the final seed, and perform a second hash process on the third data using the second hash algorithm to obtain a final pseudo-random number seed.

Optionally, the above step S3 may include the following steps S31-S33:

S31. Perform random splitting processing on the first initial seed and the second initial seed respectively, and perform splitting, shift-adding operations on equal-length information segments obtained by the random splitting processing to generate third data.

In a feasible implementation, as shown in FIG2 , the initial seed data A and the initial seed data B are randomly split, and then the equal-length information segments respectively split from the initial seed A and the initial seed B are combined according to a preset shift-and-add operation to generate data C.

S32. Perform a second hash process on the third data using a second hash algorithm to obtain a new data column, and output a random number seed according to the new data column and a preset number of output bits.

In a feasible implementation, data C is subjected to a second HASH cryptographic algorithm (which must meet certain security requirements) for a second step of hash calculation processing to obtain a new data column, and a final random number seed D that meets the requirements is output according to a set number of output bits.

S33, verify the validity of the random number seed. If it passes the validity verification, the final pseudo-random number seed is obtained; if it fails the validity verification, go to step S31.

In a feasible implementation, the final random number seed D is finally introduced into the seed validity verification module to verify the validity of the final random number seed; if the verification does not meet the requirements, it returns and repeats the above steps.

S4. Input the final pseudo-random number seed into the pseudo-random number generator to realize the generation of pseudo-random numbers.

In a feasible implementation, a flow chart of a random number seed generation method based on a financial terminal is shown in FIG3 , and a final random number seed D is used as an initialization parameter input of a designed pseudo-random number generator to realize pseudo-random number generation, and a randomness test is performed on the generated pseudo-random number information.

In order to solve the problem that the above-mentioned method of generating seeds easily leads to reduced randomness and insufficient security, the present invention proposes a new random number seed generation scheme, which introduces a chaotic mapping function to mix multiple entropy source data to ensure that the entropy source has sufficient randomness and unpredictability, and combines two different types of data information of the terminal device to generate random number seeds to ensure the security of the generated seeds, and then inputs the software random number generator to generate high-quality random numbers. This method has good randomness, anti-collision, unpredictability, verifiability and security.

For example, a method for generating a random number seed according to an embodiment of the present invention mainly includes three parts, and the steps are as follows:

The first part is the generation of the initial seed A:

A specific structure entropy pool suitable for the present invention is pre-set. For ease of understanding, its structure diagram is shown in FIG4 . The structure includes four small modules, namely, a mixed entropy source data storage module, a first-layer chaos algorithm mapping module, a second-layer chaos algorithm mapping module, and a processed data storage module. In this embodiment, the entropy pool size is set to 2048 bytes in consideration of the adaptability of the entropy pool size. Four or more entropy source data with high randomness, such as mouse movement, system time, keyboard keystrokes, and disk activity, are first collected from the system environment and stored in a pre-designed entropy pool specific storage module for mixing. The mixing operation may include XOR, shift addition, shift subtraction, etc. The specific method may be based on the needs of the scenario. In this embodiment, a relatively simple XOR operation is used to reduce memory consumption while ensuring sufficient security.

Then, the mixed multivariate data is further mapped in the first layer through the first chaotic algorithm. After the mapping is completed, the mixed mapped data is transferred to the second chaotic algorithm for the second layer mapping. The data after the two layers of mapping are transferred and stored in a specific storage module of the entropy pool; in this embodiment, the first layer chaotic mapping algorithm uses the Henon chaotic algorithm, and the second layer chaotic mapping algorithm uses the Lorenz chaotic algorithm.

In the next step, the mixed data is synchronously output to the entropy estimation module for entropy testing. If the entropy of the test result does not meet the given standard, the entropy pool update function will issue an instruction to the entropy pool to re-collect the entropy source data after detection, and repeat the previous entropy source data collection and processing steps; if the test result meets the requirements, proceed to the next step.

The data that meets the entropy estimation is calculated and processed using the first HASH algorithm that meets the security requirements, and 256-bit length data is randomly intercepted to generate the initial seed A; in this embodiment, the first hash cryptographic algorithm uses the more mature SHA256 hash algorithm from abroad; the process details are shown in Figure 5.

The second part is the generation of the initial seed B:

The internal authorization interface is used to first collect the first 32 bytes of data from the internal non-volatile random access memory of the terminal device, and the unique identification information of the terminal device is intercepted cyclically according to the length of 32 bytes, and then stored in the terminal information memory; in the current embodiment, the terminal device selects a financial terminal device, and selects five types of identification information contained in the transaction identification information unique to the financial terminal device environment, including the transaction serial number, terminal transaction sequence number, authorization code, merchant order identification, and issuing bank reference number, and two of them are intercepted in each cycle as transaction identification information; after storing the collected two types of random information in the terminal information memory, an interception shift mixing operation is performed. In this embodiment, the intercepted information length is set to 256 bits. Referring to the domestic standard initialization parameter input character string, it should have at least 128 bits, or the expected probability of repetition is not greater than ; Store the mixed information string into the specific storage area of the module as the initial seed B, waiting for calling; in this embodiment, there is also an internal update function to perform real-time detection on the terminal information storage module. When the random information inside the module is called once, an update instruction is issued to the module to collect the terminal information again; and the update function will also periodically update the terminal information collection module at a preset time, and the two update modes do not conflict; the process details are shown in Figure 6.

The third part is the generation of the final seed D:

Here, an initial seed processing module is set, and the interface calling function is used to call the two information bit strings of initial seed A and initial seed B from the processed data storage module in the first part of the entropy pool and the second part of the terminal information storage module, respectively, and perform splitting and shift addition operations; in the embodiment of the present invention, it is set to first split the initial seed A and initial seed B into two information strings of the same length, and then each information segment is shifted left by 3 bits, and then the two equal-length information generated by A and the two equal-length information generated by B are randomly matched with each other for addition calculation and merging, and the specific structural process is shown in Figure 7; thereby obtaining information bit string data C; then the obtained data C is imported into the cryptographic technology processing module, and hash calculation is performed through the second HASH cryptographic algorithm, and the obtained data result randomly intercepts 256 bits in length as the final random number seed; in this embodiment, considering security, the mature domestic SM3 cryptographic algorithm is used for hash calculation; finally, the seed verification module is required to verify the randomness of the final seed; if it does not meet the standard, return to the above steps; if it meets the standard, it is used as an initialization parameter input into the set pseudo-random number generator to generate high-quality random numbers. The details of the process are shown in Figure 7.

Furthermore, a password with high randomness and sufficient security is generated based on the generated high-quality random number for data verification and registration and login functions.

In a feasible implementation manner, the random number generation method of the present application can be applied not only to the security field, but also to fields such as mathematical statistics, physical applications, etc.

Specifically, in the field of cryptographic technology: as a security component, high-quality random numbers can be generated, which can be used to generate security parameters such as keys and trial vectors, or applied to randomization algorithms, digital signatures, encrypted communications, message authentication, etc.

Simulation and Modeling: Using random numbers to generate random weather conditions to study climate change and meteorological models.

Finance: Random numbers can be used to simulate stock prices, currency exchange rates, and other market variables.

Game Development: In the field of games, random numbers are widely used to generate random events, maps, etc. By using random numbers, games can increase variability and challenge, making the player's experience more attractive.

Experimental design: In scientific research, random numbers can be used in experimental design and data sampling.

In statistics, random numbers are also used to generate random samples for inference and modeling.

In the embodiment of the present invention, multiple high-quality entropy source data of the system environment are collected, and a mature chaos algorithm is used to mix the data to generate a random seed, thereby ensuring the randomness of the random seed generation and the unpredictability of the random number seed generation.

Two mature domestic and foreign HASH algorithms are adopted to ensure the collision resistance of the generated random seeds.

A method of combining two types of random information in a unique environment of a terminal device to generate a preliminary seed is adopted, thereby improving the security of the random number seed.

The use of minimum entropy estimation method and seed validity verification module ensures that the generated random number seeds meet domestic standards and ensures the security of random number seeds.

And the random number seed can be publicly verified intuitively through the seed validity verification results.

FIG8 and FIG9 are block diagrams of a pseudo-random number generating device according to an exemplary embodiment, and the device is used in a pseudo-random number generating method. Referring to FIG8 , the device includes a first generating module 810, a second generating module 820, a seed generating module 830, and an output module 840. For ease of explanation, FIG8 only shows the main components of the pseudo-random number generating device:

The first generation module 810 is used to construct an entropy pool, use the entropy pool to collect entropy source data from the system environment, and perform layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data, and then generate a first initial seed.

The second generating module 820 is used to collect various random data inside the terminal device by using the terminal storage module, perform interception, shift and mixing operations on the various random data, and generate a second initial seed.

The seed generation module 830 is used to generate third data according to the first initial seed and the second initial seed, and perform a second hash process on the third data using a second hash algorithm to obtain a final pseudo-random number seed.

The output module 840 is used to input the final pseudo-random number seed into the pseudo-random number generator to realize the generation of pseudo-random numbers.

Optionally, the entropy pool includes: a mixed entropy source data storage module, a first-layer chaos algorithm mapping module, a second-layer chaos algorithm mapping module, and a processed data storage module.

Among them, the mixed entropy source data storage module is used to store the entropy source data collected from the system environment, and mix the entropy source data to obtain mixed multivariate data.

The first-layer chaos algorithm mapping module is used to use the first chaos algorithm to perform the first-layer mapping on the mixed multivariate data to obtain the first-layer mapped data.

The second layer chaos algorithm mapping module is used to use the second chaos algorithm to perform the second layer mapping on the data after the first layer mapping to obtain the mixed entropy source data.

The processed data storage module is used to store the mixed entropy source data.

Optionally, the first chaos algorithm is Henon chaos algorithm.

The second chaos algorithm is the Lorenz chaos algorithm.

Optionally, the first generating module 810 is further configured to:

S11. Collect entropy source data from the system environment using an entropy pool, and perform layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data.

S12, perform an entropy estimation test on the mixed entropy source data to obtain the entropy source data after the test, and determine whether the entropy source data after the test meets the randomness requirements. If yes, execute step S13; if not, go to execute step S11.

S13. Use a first hash algorithm to perform a first step hash processing on the entropy source data that meets the randomness requirements to generate a first initial seed.

Optionally, the second generating module 820 is further configured to:

S21. Use the terminal storage module to collect random data of a preset byte length inside the terminal device, and randomly intercept unique identification information of the terminal device according to the preset byte length.

S22, performing a truncation, shift and mixing operation on the random data and the unique identification information to generate a second initial seed.

Optionally, the seed generation module 830 is further configured to:

S31, performing random splitting processing on the first initial seed and the second initial seed respectively, performing shift-addition operation on equal-length information segments obtained by the random splitting processing, and generating third data.

S32. Perform a second hash process on the third data using a second hash algorithm to obtain a new data column, and output a random number seed according to the new data column and a preset number of output bits.

S33, verify the validity of the random number seed. If it passes the validity verification, the final pseudo-random number seed is obtained; if it fails the validity verification, go to step S31.

In the embodiment of the present invention, multiple high-quality entropy source data of the system environment are collected, and a mature chaos algorithm is used to mix the data to generate a random seed, thereby ensuring the randomness of the random seed generation and the unpredictability of the random number seed generation.

Two mature domestic and foreign HASH algorithms are adopted to ensure the collision resistance of the generated random seeds.

A method of combining two types of random information in a unique environment of a terminal device to generate a preliminary seed is adopted, thereby improving the security of the random number seed.

The use of minimum entropy estimation method and seed validity verification module ensures that the generated random number seeds meet domestic standards and ensures the security of random number seeds.

And the random number seed can be publicly verified intuitively through the seed validity verification results.

FIG10 is a schematic diagram of the structure of a pseudo-random number generating device provided by an embodiment of the present invention. As shown in FIG10 , the pseudo-random number generating device may include the pseudo-random number generating apparatus shown in FIG8 . Optionally, the pseudo-random number generating device 1010 may include a processor 2001 .

Optionally, the pseudo-random number generating device 1010 may further include a memory 2002 and a transceiver 2003 .

The processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

The following is a detailed introduction to the various components of the pseudo-random number generating device 1010 in conjunction with FIG. 10 :

The processor 2001 is the control center of the pseudo-random number generating device 1010, and may be a processor or a general term for multiple processing elements. For example, the processor 2001 is one or more central processing units (CPUs), or may be application specific integrated circuits (ASICs), or may be one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

Optionally, the processor 2001 may perform various functions of the pseudo-random number generating device 1010 by running or executing a software program stored in the memory 2002 , and calling data stored in the memory 2002 .

In a specific implementation, as an embodiment, the processor 2001 may include one or more CPUs, such as CPU0 and CPU1 shown in FIG. 10 .

In a specific implementation, as an embodiment, the pseudo-random number generating device 1010 may also include multiple processors, such as the processor 2001 and the processor 2004 shown in FIG10 . Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and/or processing cores for processing data (such as computer program instructions).

The memory 2002 is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the processor 2001, or may exist independently, and be coupled to the processor 2001 through an interface circuit (not shown in FIG. 10 ) of the pseudo-random number generation device 1010, which is not specifically limited in the embodiment of the present invention.

The transceiver 2003 is used to communicate with a network device or a terminal device.

Optionally, the transceiver 2003 may include a receiver and a transmitter (not shown separately in FIG. 10 ), wherein the receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

Optionally, the transceiver 2003 may be integrated with the processor 2001 or exist independently and be coupled to the processor 2001 via an interface circuit (not shown in FIG. 10 ) of the pseudo-random number generating device 1010 , which is not specifically limited in the embodiment of the present invention.

It should be noted that the structure of the pseudo-random number generation device 1010 shown in FIG. 10 does not constitute a limitation on the router, and the actual knowledge structure identification device may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

In addition, the technical effects of the pseudo-random number generation device 1010 can refer to the technical effects of the pseudo-random number generation method described in the above method embodiment, and will not be repeated here.

It should be understood that the processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

It should be understood that the term "and/or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character "/" in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and/or" relationship. Please refer to the context for specific understanding.

In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

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

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (6)

1. A method of generating pseudo-random numbers, the method comprising:

S1, constructing an entropy pool, collecting entropy source data from a system environment by using the entropy pool, and performing layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data, so as to generate a first initial seed;

s2, collecting various random data in the terminal equipment by using a terminal storage module, intercepting, shifting and mixing the various random data, and generating a second initial seed;

S3, generating third data according to the first initial seed and the second initial seed, and performing second-step hash processing on the third data by using a second hash algorithm to obtain a final pseudo-random number seed;

S4, inputting the final pseudo-random number seeds into a pseudo-random number generator to realize the generation of pseudo-random numbers;

The entropy pool in S1 includes: the system comprises a mixed entropy source data storage module, a first-layer chaotic algorithm mapping module, a second-layer chaotic algorithm mapping module and a processed data storage module;

The mixed entropy source data storage module is used for storing entropy source data collected from a system environment and mixing the entropy source data to obtain mixed multi-element data;

The first layer chaotic algorithm mapping module is used for performing first layer mapping on the mixed multi-element data by using a first chaotic algorithm to obtain data after the first layer mapping;

The second-layer chaotic algorithm mapping module is used for performing second-layer mapping on the data after the first-layer mapping by using a second chaotic algorithm to obtain mixed entropy source data;

the processed data storage module is used for storing the mixed entropy source data;

The step S1 of collecting entropy source data from a system environment by using the entropy pool, and performing layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data, thereby generating a first initial seed, comprising:

S11, collecting entropy source data from a system environment by utilizing the entropy pool, and performing layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data;

S12, performing entropy estimation test on the mixed entropy source data to obtain tested entropy source data, judging whether the tested entropy source data meets the randomness requirement, and if so, executing a step S13; if not, go to execute step S11;

s13, performing first-step hash processing on entropy source data meeting the randomness requirement by using a first hash algorithm to generate a first initial seed;

In the step S3, third data is generated according to the first initial seed and the second initial seed, and a second hash algorithm is used to perform a second hash process on the third data, so as to obtain a final pseudo-random number seed, which includes:

S31, respectively carrying out random splitting treatment on the first initial seed and the second initial seed, and carrying out shift adding operation on the equal-length information segments obtained by the random splitting treatment to generate third data;

S32, performing second-step hash processing on the third data by using a second hash algorithm to obtain a new data column, and outputting a random number seed according to a preset output bit number according to the new data column;

S33, verifying the validity of the random number seeds, and if the random number seeds pass the validity verification, obtaining final pseudo-random number seeds; if the validity verification is not passed, the process goes to step S31.

2. The method of claim 1, wherein the first chaotic algorithm is an englon Henon chaotic algorithm;

the second chaotic algorithm is a Lorenz chaotic algorithm.

3. The method according to claim 1, wherein the collecting, by the terminal storage module, a plurality of random data in the terminal device in S2, performing an intercept-shift mixing operation on the plurality of random data, and generating a second initial seed includes:

S21, collecting random data with preset byte length in terminal equipment by using a terminal storage module, and randomly intercepting specific identification information of the terminal equipment according to the preset byte length;

s22, intercepting, shifting and mixing the random data and the specific identification information to generate a second initial seed.

4. A pseudo-random number generating apparatus for implementing the pseudo-random number generating method according to any one of claims 1-3, characterized in that the apparatus comprises:

the first generation module is used for constructing an entropy pool, collecting entropy source data from a system environment by using the entropy pool, carrying out layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data, and further generating a first initial seed;

the second generation module is used for collecting various random data in the terminal equipment by utilizing the terminal storage module, intercepting, shifting and mixing the various random data, and generating a second initial seed;

the seed generation module is used for generating third data according to the first initial seed and the second initial seed, and performing second-step hash processing on the third data by using a second hash algorithm so as to obtain a final pseudo-random number seed;

The output module is used for inputting the final pseudo-random number seeds into a pseudo-random number generator to realize the generation of pseudo-random numbers;

the entropy pool comprises: the system comprises a mixed entropy source data storage module, a first-layer chaotic algorithm mapping module, a second-layer chaotic algorithm mapping module and a processed data storage module;

The mixed entropy source data storage module is used for storing entropy source data collected from a system environment and mixing the entropy source data to obtain mixed multi-element data;

The first layer chaotic algorithm mapping module is used for performing first layer mapping on the mixed multi-element data by using a first chaotic algorithm to obtain data after the first layer mapping;

The second-layer chaotic algorithm mapping module is used for performing second-layer mapping on the data after the first-layer mapping by using a second chaotic algorithm to obtain mixed entropy source data;

the processed data storage module is used for storing the mixed entropy source data;

Collecting entropy source data from a system environment by using the entropy pool, performing layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data, and further generating a first initial seed, wherein the method comprises the following steps of:

S11, collecting entropy source data from a system environment by utilizing the entropy pool, and performing layer-by-layer mixed mapping on the entropy source data to obtain mixed entropy source data;

S12, performing entropy estimation test on the mixed entropy source data to obtain tested entropy source data, judging whether the tested entropy source data meets the randomness requirement, and if so, executing a step S13; if not, go to execute step S11;

s13, performing first-step hash processing on entropy source data meeting the randomness requirement by using a first hash algorithm to generate a first initial seed;

generating third data according to the first initial seed and the second initial seed, performing a second hash processing on the third data by using a second hash algorithm, and further obtaining a final pseudo-random number seed, including:

S31, respectively carrying out random splitting treatment on the first initial seed and the second initial seed, and carrying out shift adding operation on the equal-length information segments obtained by the random splitting treatment to generate third data;

S32, performing second-step hash processing on the third data by using a second hash algorithm to obtain a new data column, and outputting a random number seed according to a preset output bit number according to the new data column;

S33, verifying the validity of the random number seeds, and if the random number seeds pass the validity verification, obtaining final pseudo-random number seeds; if the validity verification is not passed, the process goes to step S31.

5. A pseudo-random number generating device, characterized in that the pseudo-random number generating device comprises:

A processor;

a memory having stored thereon computer readable instructions which, when executed by the processor, implement the method of any of claims 1 to 3.

6. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program code, which is callable by a processor for executing the method according to any one of claims 1 to 3.