Random number generation protection method and system based on electronic hardware thermal noise

Abstract

The invention discloses a random number generation protection method and system based on electronic hardware thermal noise, and relates to the technical field of network information, and the method comprises the steps: collecting a thermal noise signal in electronic hardware, filtering the thermal noise signal through a low-pass filter, and processing the filtered thermal noise signal through a low-noise amplifier; quantizing the processed thermal noise signal, and introducing equivalent noise to carry out random number mixing on the quantized thermal noise signal; and the mixed random numbers are mapped into a cryptographic protocol and are combined, so that the protection capability of a cryptographic algorithm is improved. According to the invention, the randomness and security of the random number are effectively improved, the predictability and periodicity problems of a traditional pseudo-random number generator are avoided, the random number is applied to the key updating and management process, the protection capability of a cryptographic algorithm is improved, and an important technical breakthrough is brought to the field of information security.

Description

Technical Field

[0001] The present invention relates to the field of network information technology, and in particular to a random number generation protection method and system based on electronic hardware thermal noise. Background Art

[0002] The current society is in an era of rapid development of informatization, and information security is increasingly valued. With the popularization of applications such as network communications and electronic transactions, the demand for generating high-quality random numbers has become more urgent. Against this background, a random number generation protection method based on electronic hardware thermal noise has emerged to improve the protection capability of cryptographic algorithms and ensure information security.

[0003] Traditional random number generation methods have security risks such as pseudo-randomness and predictability. In addition, the hardware implementation of random number generation in the prior art often has instability and predictability of noise signals and is vulnerable to external attacks or interference. The present invention collects thermal noise signals inside electronic hardware and combines equivalent noise and mixing technologies to solve the security problems existing in traditional random number generation methods and improve the protection capabilities of cryptographic algorithms. Summary of the invention

[0004] In view of the problems existing in the existing random number generation protection method and system based on thermal noise of electronic hardware, the present invention is proposed.

[0005] Therefore, the present invention aims at the instability and predictability problems of random number generation in the prior art, and adopts the random number generation protection method based on electronic hardware thermal noise of the present invention, which improves the randomness and security of random numbers by mixing equivalent noise and quantized thermal noise signals.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a random number generation protection method based on thermal noise of electronic hardware, which includes collecting a thermal noise signal inside the electronic hardware, filtering the thermal noise signal using a low-pass filter, and processing the filtered thermal noise signal using a low-noise amplifier;

[0008] The processed thermal noise signal is quantized, and equivalent noise is introduced to perform random number mixing on the quantized thermal noise signal;

[0009] The mixed random numbers are mapped to the cryptographic protocol and combined to improve the protection capabilities of the cryptographic algorithm.

[0010] As a preferred solution of the random number generation protection method based on electronic hardware thermal noise of the present invention, wherein: the thermal noise signal collected inside the electronic hardware includes a quantum security service end and a terminal node installed with a quantum security card, the quantum security service end is connected to the terminal node through a traditional network, and the quantum security service end includes a key center subnode and an access base station subnode;

[0011] The filtering includes authenticating the terminal node according to the access base station subnode, matching the key center subnode based on the terminal node, and feeding back the address information of the key center subnode to the terminal node;

[0012] The address information after feedback is extracted and stored, and the terminal nodes that have not received feedback are filtered.

[0013] As a preferred solution of the random number generation protection method based on electronic hardware thermal noise of the present invention, the processing includes connecting a low noise amplifier with a thermal noise signal, inputting the signal output by the filtered terminal node to the input end of the amplifier, calculating the amplifier gain value, calculating the output signal amplitude range and the input signal amplitude range based on the gain value, and using a nonlinear function to describe the output signal amplitude range. and input signal amplitude range V in the relationship between;

[0014] The specific calculation formula for calculating the amplifier gain value is:

[0015]

[0016] Where s represents the operating frequency of the amplifier, s0 represents the cutoff frequency of the amplifier, and n represents the order;

[0017] when When the low value of the output signal is less than or equal to the preset low output value, the amplifier gain is increased to improve the output signal amplitude, and the increasing amplifier gain includes adjusting the amplifier parameters, increasing the input voltage or adjusting the component values ​​in the amplifier circuit;

[0018] when When the low value of the output signal is greater than the preset low output value, the amplifier gain is reduced and the output signal amplitude is reduced. The reducing the amplifier gain includes adjusting the amplifier parameters, reducing the input voltage or adjusting the component values ​​in the amplifier circuit.

[0019] The specific formula for the input signal amplitude range is:

[0020]

[0021] Where k1 and k2 represent nonlinear factors, A represents the amplifier gain value, and Vout Indicates the output signal amplitude range, V in Indicates the input signal amplitude range.

[0022] As a preferred solution of the random number generation protection method based on electronic hardware thermal noise of the present invention, the quantization includes adjusting the ratio of the output signal amplitude range to the input signal amplitude range by using a quantizer, and the adjustment includes collecting a calibration data set by an analog signal generator of an external test instrument, inputting the collected calibration data set into a polynomial fitting algorithm, and analyzing the residual between the fitting curve and the actual data. The specific formula of the fitting algorithm is:

[0023]

[0024] Among them, y actual Indicates the actual output value of the output signal amplitude range, V in i represents the input signal value within the i-th input signal amplitude range, α i represents the coefficient of the fitting polynomial, ∈ represents the error term;

[0025] The specific calculation formula of the residual is:

[0026] e=y actual -y pred

[0027] Among them, y actual Indicates the actual output value of the output signal amplitude range, y pred represents the predicted value, and e represents the residual;

[0028] Fit the residual e with each input signal value within the corresponding input signal amplitude range, and observe the distribution state of the residual e;

[0029] If the residual distribution is randomly distributed, the fitting curve is not successfully fitted to the actual data. In this case, the local outlier factor algorithm is used to find discrete points in the distribution state;

[0030] If the residual distribution is uniform, the fitting curve is successfully fitted to the actual data.

[0031] As a preferred solution of the random number generation protection method based on electronic hardware thermal noise of the present invention, wherein: the finding of discrete points in the distribution state includes comparing the local density around the actual data with the local density of the adjacent data points to determine the discrete points;

[0032] The local density around the actual data includes calculating a Gaussian kernel function, and calculating the local density around the actual data based on the Gaussian kernel function, and the specific steps are as follows:

[0033] The specific formula for calculating the Gaussian kernel function is:

[0034]

[0035] Among them, μ represents the mean, σ represents the standard deviation, and K(x) represents the function;

[0036] The calculation formula of the local density around the actual data is:

[0037]

[0038] Among them, n represents the number of samples in the actual data, d represents the dimension of the data, and ||x i -x j || represents the data point x i and x j The distance between

[0039] The calculation formula of the local density of the adjacent data points is:

[0040]

[0041] Among them, |N i | represents the set of neighboring data points N i The size of V represents the volume of the data space, h represents the bandwidth parameter, which is used to adjust the width of the kernel function, and j represents the i The set of neighboring data points N i The index in ;

[0042] If β≤γ, the local density is lower than the local density of the adjacent data points, and the actual data discrete points to be fitted are found;

[0043] If β>γ, the local density is higher than the local density of the adjacent data points, which means that the actual data discrete points to be fitted are not found, and the discrete points are calculated twice;

[0044] The secondary calculation of discrete points includes calculating the average value of the local density around the actual data and the local density of the adjacent data points, and comparing the average value with the discrete points preset by the system. Conduct secondary comparisons;

[0045] The specific calculation formula of the average value is:

[0046]

[0047] like When , the actual data discrete points are extracted; otherwise, the actual data does not show discrete phenomenon.

[0048] As a preferred solution of the random number generation protection method based on electronic hardware thermal noise of the present invention, wherein: the equivalent noise includes setting the signal processing parameter τ according to the extracted actual data discrete points, and the random number mixing of the quantized thermal noise signal includes calculating the abnormality degree of the actual data discrete points;

[0049] The specific formula for calculating the actual data discrete points is:

[0050]

[0051] in, Represents data point x i The local outlier factor, N i Represents data point x i A set of neighboring data points, j represents the i The set of neighboring data points N i The index in .

[0052] As a preferred solution of the random number generation protection method based on electronic hardware thermal noise described in the present invention, wherein: the combination includes using the actually calculated data discrete points for random number initial quantity for random authentication, and the authentication includes applying it to the key update and management process of the cryptographic algorithm, monitoring the discrete point destruction, and protecting the random numbers of the hardware thermal noise.

[0053] In a second aspect, an embodiment of the present invention provides a random number generation protection system based on thermal noise of electronic hardware, which includes: a processing module, which collects thermal noise signals inside the electronic hardware, filters the thermal noise signals using a low-pass filter, and processes the filtered thermal noise signals using a low-noise amplifier;

[0054] A quantization module quantizes the processed thermal noise signal and introduces equivalent noise to perform random number mixing on the quantized thermal noise signal;

[0055] The protection module maps the mixed random numbers into the cryptographic protocol and combines them to improve the protection capability of the cryptographic algorithm.

[0056] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned random number generation protection method based on electronic hardware thermal noise is implemented.

[0057] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned random number generation protection method based on electronic hardware thermal noise is implemented.

[0058] The beneficial effects of the present invention are as follows: the random number generation protection method based on electronic hardware thermal noise of the present invention effectively improves the randomness and security of random numbers, generates more random numbers by collecting thermal noise signals inside the electronic hardware and introducing equivalent noise for mixing, utilizes the unique physical properties of the hardware, avoids the predictability and periodicity problems of traditional pseudo-random number generators, combines cryptographic protocols, applies random numbers to key update and management processes, improves the protection capabilities of cryptographic algorithms, and brings important technological breakthroughs to the field of information security. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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. Among them:

[0060] Figure 1 A flowchart of a random number generation protection method and system based on electronic hardware thermal noise is provided in accordance with an embodiment of the present invention.

[0061] Figure 2 A protection flow chart diagram of a random number generation protection method and system based on electronic hardware thermal noise is provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0063] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0064] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0065] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0066] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0067] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0068] Example 1

[0069] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a random number generation protection method based on electronic hardware thermal noise, comprising:

[0070] S1: Collect the thermal noise signal inside the electronic hardware, filter the thermal noise signal with a low-pass filter, and process the filtered thermal noise signal with a low-noise amplifier.

[0071] Among them, the thermal noise signal inside the electronic hardware is collected, including a quantum security server and a terminal node equipped with a quantum security card. The quantum security server and the terminal node are connected through a traditional network. The quantum security server includes a key center sub-node and an access base station sub-node;

[0072] The filtering includes authenticating the terminal node according to the access base station subnode, matching the key center subnode based on the terminal node, and feeding back the address information of the key center subnode to the terminal node;

[0073] The address information after feedback is extracted and stored, and the terminal nodes that have not received feedback are filtered.

[0074] S2: quantize the processed thermal noise signal, introduce equivalent noise to perform random number mixing on the quantized thermal noise signal.

[0075] The processing includes connecting a low noise amplifier to a thermal noise signal, inputting the signal output by the filtered terminal node to the input of the amplifier, calculating the amplifier gain value, calculating the output signal amplitude range and the input signal amplitude range based on the gain value, and using a nonlinear function to describe the output signal amplitude range. and input signal amplitude range V in the relationship between;

[0076] The specific calculation formula for calculating the amplifier gain value is:

[0077]

[0078] Where s represents the operating frequency of the amplifier, s0 represents the cutoff frequency of the amplifier, and n represents the order;

[0079] when When the low value of the output signal is less than or equal to the preset low output value, the amplifier gain is increased to improve the output signal amplitude. Increasing the amplifier gain includes adjusting the amplifier parameters, increasing the input voltage or adjusting the component values ​​in the amplifier circuit;

[0080] when When the low value of the output signal is greater than the preset low output value, the amplifier gain is reduced and the output signal amplitude is reduced. Reducing the amplifier gain includes adjusting the amplifier parameters, reducing the input voltage or adjusting the component values ​​in the amplifier circuit.

[0081] The specific formula for the input signal amplitude range is:

[0082]

[0083] Where k1 and k2 represent nonlinear factors, A represents the amplifier gain value, and V out Indicates the output signal amplitude range, V in Indicates the input signal amplitude range.

[0084] Furthermore, in the process of quantizing the processed thermal noise signal, a low-noise amplifier is first connected to the thermal noise signal to process the filtered terminal node output signal. The gain value of the amplifier is calculated according to the characteristics of the input signal. The order is used to calculate the gain value according to the operating frequency and the cut-off frequency of the amplifier. The gain value determines the relationship between the amplitude range of the output signal and the amplitude range of the input signal. The amplitude of the output signal is compared with a preset low output value. When the output signal amplitude is less than or equal to the preset low output value, the gain of the amplifier is increased by adjusting the amplifier parameters, increasing the input voltage or adjusting the component values ​​in the amplifier circuit to increase the output signal amplitude; conversely, when the output signal amplitude is greater than the preset low output value, the gain of the amplifier is reduced by adjusting the amplifier parameters, reducing the input voltage or adjusting the component values ​​in the amplifier circuit to reduce the output signal amplitude.

[0085] S2.1: Quantization includes adjusting the ratio of the output signal amplitude range to the input signal amplitude range using a quantizer. The adjustment includes collecting a calibration data set through an analog signal generator of an external test instrument, inputting the collected calibration data set into a polynomial fitting algorithm, and analyzing the residual between the fitting curve and the actual data. The specific formula of the fitting algorithm is:

[0086]

[0087] Among them, y actual Indicates the actual output value of the output signal amplitude range, represents the input signal value within the i-th input signal amplitude range, α i represents the coefficient of the fitting polynomial, ∈ represents the error term;

[0088] The specific calculation formula of the residual is:

[0089] e=y actual -y pred

[0090] Among them, y actual Indicates the actual output value of the output signal amplitude range, y pred represents the predicted value, and e represents the residual;

[0091] Fit the residual e with each input signal value within the corresponding input signal amplitude range, and observe the distribution state of the residual e;

[0092] If the residual distribution is randomly distributed, the fitting curve is not successfully fitted to the actual data. In this case, the local outlier factor algorithm is used to find discrete points in the distribution state;

[0093] If the residual distribution is uniform, the fitting curve is successfully fitted to the actual data.

[0094] Furthermore, during the quantization process, about 100 calibration data points were collected. The data points represent the actual output values ​​under various input signal amplitude ranges. Through the fitting algorithm, a third-order polynomial model was used to approximate the relationship between these data points in order to better understand the corresponding relationship between the input signal amplitude range and the output signal amplitude range. During the fitting process, the residuals between the fitting curve and the actual data were analyzed to evaluate the accuracy of the fitting. If the residuals are randomly distributed, it means that the fitting curve fails to fit the actual data well. The local outlier factor algorithm will be used to find discrete points in the distribution state. On the contrary, if the residuals are uniformly distributed, it means that the fitting curve successfully approximates the actual data and the fitting effect is good. The quantified fitting data is shown in Table 1 below:

[0095] Table 1 Quantitative fitting data table

[0096] Serial number Input signal amplitude range Actual output value 1 0.5 0.48 2 1.0 1.02 3 1.5 1.45 4 2.0 1.98 5 2.5 2.42 6 3.0 2.97 7 3.5 3.48 8 4.0 4.03 9 4.5 4.52 10 5.0 5.07 ... ... ... 100 50.0 50.35

[0097] This table shows about 100 calibration data points collected, representing the actual output values ​​under various input signal amplitude ranges. By fitting a third-order polynomial model, we try to approximate the relationship between the input signal amplitude range and the output signal amplitude range. By analyzing the residual between the fitting curve and the actual data, we can evaluate the accuracy of the fitting and then determine the quality of the fitting effect.

[0098] S2.2: Finding discrete points in a distribution state includes comparing the local density around the actual data with the local density of neighboring data points to determine the discrete points;

[0099] The local density around the actual data includes calculating the Gaussian kernel function, and calculating the local density around the actual data based on the Gaussian kernel function. The specific steps are as follows:

[0100] The specific formula for calculating the Gaussian kernel function is:

[0101]

[0102] Among them, μ represents the mean, σ represents the standard deviation, and K(x) represents the function;

[0103] The local density around the actual data is calculated as:

[0104]

[0105] Among them, n represents the number of samples in the actual data, d represents the dimension of the data, and ||x i -x j || represents the data point x i and x j The distance between

[0106] The local density of neighboring data points is calculated as:

[0107]

[0108] Among them, |N i | represents the set of neighboring data points N i The size of V represents the volume of the data space, h represents the bandwidth parameter, which is used to adjust the width of the kernel function, and j represents the i The set of neighboring data points N i The index in ;

[0109] If β≤γ, the local density is lower than the local density of the adjacent data points, and the actual data discrete points to be fitted are found;

[0110] If β>γ, the local density is higher than the local density of the adjacent data points, which means that the actual data discrete points to be fitted are not found, and the discrete points are calculated twice;

[0111] The secondary calculation of discrete points includes calculating the average value of the local density around the actual data and the local density of the adjacent data points, and then comparing the average value with the discrete points preset by the system. Conduct secondary comparisons;

[0112] The specific calculation formula for the average value is:

[0113]

[0114] like When , the actual data discrete points are extracted; otherwise, the actual data does not show discrete phenomenon.

[0115] S2.3: Equivalent noise includes setting the signal processing parameter τ according to the extracted actual data discrete points, and performing random number mixing on the quantized thermal noise signal includes calculating the abnormality degree of the actual data discrete points;

[0116] The specific formula for calculating the actual data discrete points is:

[0117]

[0118] Among them, LOF(x i ) represents the data point x i The local outlier factor, N i Represents data point x i A set of neighboring data points, j represents the i The set of neighboring data points N i The index in .

[0119] Furthermore, the combination includes using the actual calculated data discrete points for random number initialization to perform random authentication, and the authentication includes applying it to the key update and management process of the cryptographic algorithm, monitoring the discrete point damage, and protecting the random numbers of hardware thermal noise.

[0120] In a preferred embodiment, a random number generation protection system based on thermal noise of electronic hardware includes a processing module that collects thermal noise signals inside the electronic hardware, filters the thermal noise signals using a low-pass filter, and processes the filtered thermal noise signals using a low-noise amplifier;

[0121] A quantization module quantizes the processed thermal noise signal and introduces equivalent noise to perform random number mixing on the quantized thermal noise signal;

[0122] The protection module maps the mixed random numbers into the cryptographic protocol and combines them to improve the protection capability of the cryptographic algorithm.

[0123] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0124] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0125] In summary, the random number generation protection method based on electronic hardware thermal noise of the present invention effectively improves the randomness and security of random numbers. It generates more random numbers by collecting thermal noise signals inside the electronic hardware and introducing equivalent noise for mixing. It utilizes the unique physical properties of the hardware and avoids the predictability and periodicity problems of traditional pseudo-random number generators. In combination with cryptographic protocols, random numbers are applied to key update and management processes, which improves the protection capabilities of cryptographic algorithms and brings important technological breakthroughs to the field of information security.

[0126] Example 2

[0127] Reference Figure 1 and Figure 2 , which is the second embodiment of the present invention, and this embodiment provides a random number generation protection method based on electronic hardware thermal noise. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0128] The experiment first starts with collecting thermal noise signals inside the electronic hardware, filtering these signals with a low-pass filter, and then using a low-noise amplifier to process the filtered signals. Next, the processed thermal noise signals are quantized, and equivalent noise is introduced for random number mixing. During the quantization process, about 100 calibration data points are collected, representing the actual output values ​​under various input signal amplitude ranges. Through the fitting algorithm, a third-order polynomial model is used to approximate the relationship between these data points in order to better understand the correspondence between the input signal amplitude range and the output signal amplitude range.

[0129] During the fitting process, the residuals between the fitting curve and the actual data were analyzed to evaluate the accuracy of the fitting. If the residuals were randomly distributed, it meant that the fitting curve failed to fit the actual data well. The local outlier factor algorithm was used to find discrete points in the distribution state. On the contrary, if the residuals were uniformly distributed, it meant that the fitting curve successfully approximated the actual data and the fitting effect was good. The experimental data comparison is shown in Table 2 below:

[0130] Table 2 Experimental data comparison table

[0131] Calibration data points Input signal amplitude range Actual output value 1 0.2 0.18 2 0.4 0.37 3 0.6 0.59 4 0.8 0.76 5 1.0 1.05 ... ... ... 100 20.0 19.85

[0132] The above table shows the comparison between multiple groups of data collected in the experiment. By comparing the data under different conditions, we can clearly observe the influence of various experimental parameters on the results. These data comparisons help us better understand the role of different factors in the experimental process, thereby providing an important reference for further analysis and experimental design. The comparison with the existing technology is shown in Table 3 below:

[0133] Table 3 Comparison with prior art

[0134]

[0135] From the above comparison, it can be seen that the present invention has significant advantages in the quality of random number generation, the precision of signal processing, outlier detection and processing, random number mixing method and cryptographic algorithm protection capability, and can greatly improve the security and reliability of the random number generation system.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A random number generation protection method based on electronic hardware thermal noise, characterized in that: include, Collect the thermal noise signal inside the electronic hardware, filter the thermal noise signal with a low-pass filter, and process the filtered thermal noise signal with a low-noise amplifier; The processed thermal noise signal is quantized, and equivalent noise is introduced to perform random number mixing on the quantized thermal noise signal; The mixed random numbers are mapped to the cryptographic protocol and combined to improve the protection capabilities of the cryptographic algorithm.

2. The random number generation protection method based on electronic hardware thermal noise according to claim 1, characterized in that: The thermal noise signal inside the electronic hardware is collected, including a quantum security service end and a terminal node equipped with a quantum security card, wherein the quantum security service end is connected to the terminal node through a traditional network, and the quantum security service end includes a key center subnode and an access base station subnode; The filtering includes authenticating the terminal node according to the access base station subnode, matching the key center subnode based on the terminal node, and feeding back the address information of the key center subnode to the terminal node; The address information after feedback is extracted and stored, and the terminal nodes that have not received feedback are filtered.

3. The random number generation protection method based on electronic hardware thermal noise as claimed in claim 2, characterized in that: The processing includes connecting a low noise amplifier to a thermal noise signal, inputting a signal output by a filtered terminal node to an input of the amplifier, calculating a gain value of the amplifier, calculating an output signal amplitude range and an input signal amplitude range based on the gain value, and using a nonlinear function to describe the output signal amplitude range. and input signal amplitude range V in the relationship between; The specific calculation formula for calculating the amplifier gain value is: Where s represents the operating frequency of the amplifier, s0 represents the cutoff frequency of the amplifier, and n represents the order; when When the low value of the output signal is less than or equal to the preset low output value, the amplifier gain is increased to improve the output signal amplitude, and the increasing amplifier gain includes adjusting the amplifier parameters, increasing the input voltage or adjusting the component values ​​in the amplifier circuit; when When the low value of the output signal is greater than the preset low output value, the amplifier gain is reduced to reduce the output signal amplitude, wherein the reducing the amplifier gain includes adjusting the amplifier parameters, reducing the input voltage or adjusting the component values ​​in the amplifier circuit; The specific formula for the input signal amplitude range is: Where k1 and k2 represent nonlinear factors, A represents the amplifier gain value, and V out Indicates the output signal amplitude range, V in Indicates the input signal amplitude range.

4. The random number generation protection method based on electronic hardware thermal noise as claimed in claim 3, characterized in that: The quantization includes adjusting the ratio of the output signal amplitude range to the input signal amplitude range by using a quantizer, and the adjustment includes collecting a calibration data set by using an analog signal generator of an external test instrument, inputting the collected calibration data set into a polynomial fitting algorithm, and analyzing the residual between the fitting curve and the actual data. The specific formula of the fitting algorithm is: Among them, y actual Indicates the actual output value of the output signal amplitude range, V in i represents the input signal value within the i-th input signal amplitude range, α i represents the coefficient of the fitting polynomial, ∈ represents the error term; The specific calculation formula of the residual is: e=y actual -and pred Among them, y actual Indicates the actual output value of the output signal amplitude range, y pred represents the predicted value, and e represents the residual; Fit the residual e with each input signal value within the corresponding input signal amplitude range, and observe the distribution state of the residual e; If the residual distribution is randomly distributed, the fitting curve is not successfully fitted to the actual data. In this case, the local outlier factor algorithm is used to find discrete points in the distribution state; If the residual distribution is uniform, the fitting curve is successfully fitted to the actual data.

5. The random number generation protection method based on electronic hardware thermal noise according to claim 4, characterized in that: The finding of discrete points in the distribution state includes comparing the local density around the actual data with the local density of the adjacent data points to determine the discrete points; The local density around the actual data includes calculating a Gaussian kernel function, and calculating the local density around the actual data based on the Gaussian kernel function, and the specific steps are as follows: The specific formula for calculating the Gaussian kernel function is: Among them, μ represents the mean, σ represents the standard deviation, and K(x) represents the function; The calculation formula of the local density around the actual data is: Among them, n represents the number of samples in the actual data, d represents the dimension of the data, and ||x i -x j || represents the data point x i and x j The distance between The calculation formula of the local density of the adjacent data points is: Among them, |N i | represents the set of neighboring data points N i The size of V represents the volume of the data space, h represents the bandwidth parameter, which is used to adjust the width of the kernel function, and j represents the i The set of neighboring data points N i The index in ; If β≤γ, the local density is lower than the local density of the adjacent data points, and the actual data discrete points to be fitted are found; If β>γ, the local density is higher than the local density of the adjacent data points, which means that the actual data discrete points to be fitted are not found, and the discrete points are calculated twice; The secondary calculation of discrete points includes calculating the average value of the local density around the actual data and the local density of the adjacent data points, and comparing the average value with the discrete points preset by the system. Conduct secondary comparisons; The specific calculation formula of the average value is: like When , the actual data discrete points are extracted; otherwise, the actual data does not show discrete phenomenon.

6. The random number generation protection method based on electronic hardware thermal noise according to claim 5, characterized in that: The equivalent noise includes setting a signal processing parameter τ according to the extracted actual data discrete points, and the random number mixing of the quantized thermal noise signal includes calculating the abnormality degree of the actual data discrete points; The specific formula for calculating the actual data discrete points is: Among them, LOF(x i ) represents the data point x i The local outlier factor, N i Represents data point x i A set of neighboring data points, j represents the i The set of neighboring data points N i The index in .

7. The random number generation protection method based on electronic hardware thermal noise according to claim 6, characterized in that: The combination includes using actually calculated data discrete points for random number initial quantity to perform random authentication, and the authentication includes applying it to the key update and management process of the cryptographic algorithm, monitoring the discrete point damage, and protecting the random number of the hardware thermal noise.

8. A random number generation protection system based on electronic hardware thermal noise, based on the random number generation protection method based on electronic hardware thermal noise according to any one of claims 1 to 7, characterized in that: include, A processing module collects thermal noise signals inside the electronic hardware, filters the thermal noise signals using a low-pass filter, and processes the filtered thermal noise signals using a low-noise amplifier; A quantization module quantizes the processed thermal noise signal and introduces equivalent noise to perform random number mixing on the quantized thermal noise signal; The protection module maps the mixed random numbers into the cryptographic protocol and combines them to improve the protection capability of the cryptographic algorithm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the random number generation protection method based on electronic hardware thermal noise according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the random number generation protection method based on electronic hardware thermal noise according to any one of claims 1 to 7 are implemented.

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