System for generating random numbers based on blood samples and method using same

Abstract

The present invention provides a system and method that is capable of generating random numbers at high speed while maximizing cost efficiency, and that ensures the reliability of the generated random numbers by improving the randomness of the random numbers generated based on blood samples. [Solution] A system for generating random numbers based on a blood sample includes a sample loading module that supplies a predetermined blood sample, a light illumination module provided by the sample loading module that illuminates light onto the blood sample moved to a predetermined region of interest (ROI), an image generation module that uses the light illumination module to capture an image of the blood sample located in the region of interest in a predetermined manner to obtain an original speckle image, and a random number generation module that processes the original speckle image generated from the image generation module in a predetermined manner to generate random numbers.

Description

[Technical field]

[0001] The present invention relates to a system for generating random numbers based on a blood sample and a method using the same, and more particularly, to a technology related to a random number generation system to which the 2P-TO-VN (Two-Pass Tuple-Output von Neumann) method is applied. [Background technology]

[0002] The material described in this section is merely intended to provide background information regarding the present invention and may not constitute prior art.

[0003] As the use of wired and wireless communications, including the Internet, rapidly expands, the issue of security in communication networks is becoming increasingly important in terms of protecting important national, corporate and financial secrets, as well as protecting personal privacy. It is believed that cryptosystems based on mathematical computational complexity, such as PSA, can be fundamentally decrypted if quantum computers are developed, and so countermeasures are required.

[0004] Compared to cryptosystems based on computational complexity, the encryption method using One Time Pad (OTP) is known to be the most secure encryption method. If a stream cipher is generated using the One Time Pad, fast and secure encrypted communication can be performed. A Random Number Generator (RNG) is used to generate such a One Time Pad.

[0005] The evolution and continued development of random number generator systems is a critical foundation for the future of the cryptography field. Such RNG systems are at the heart of all cryptographic systems due to their role in generating keys that are not only difficult to predict, but nearly impossible to crack.

[0006] Such unpredictability can ensure a high level of security, a necessary feature in today's digital world. There are two main types of random number generators: algorithmic and physical. Algorithmic generators, also known as PseudoRandom Number Generators (PRNG), use complex mathematical formulas to generate a sequence of numbers.

[0007] Although PRNGs are convenient and efficient, they have the disadvantage that the random numbers they generate can be reverse engineered and predicted, even if they pass randomness tests. In contrast, physical (or real) random number generators can derive randomness from probabilistic physical processes. Such processes are theoretically predictable with perfect information, but practically unpredictable due to limitations in time and computational resources.

[0008] Due to these properties, RNGs play a key role in a variety of cybersecurity tasks, including key generation, digital signature generation, initialization vector generation for encryption, and salt value generation for security preservation, and can address the cybersecurity requirements of interconnected systems beyond the IoT domain.

[0009] One emerging area of ​​interest is the use of optical and physical copy protection for enhanced security. Work by Di Falco et al. shows that a disordered system facilitated by silicon chips can be exploited to achieve a cryptographic system with perfect secrecy.

[0010] However, it is important to note that physics-based RNGs have inherent challenges. For example, capturing arbitrary patterns from lava lamps is an innovative method but is bandwidth limited. Other methods using light-emitting diodes and cell phone cameras capture randomness from quantum fluctuations of light but are very complex to set up. Thus, the landscape for RNG systems has undergone change in recent years, revealing that traditional artificially generated RNGs contain inherent security vulnerabilities.

[0011] These vulnerabilities have inspired a shift towards biomimetic RNG systems that are more unpredictable and inherently random -- a shift that acknowledges the fact that biological systems often contain a level of complexity and randomness that is difficult, if not impossible, to replicate artificially.

[0012] Therefore, recent RNG technology attempts to focus on the generation of RNGs using speckle patterns. The main goal of such technology is to achieve a balance between economy and portability, both of which are considered essential factors for the widespread adoption and application of such systems.

[0013] In the area of ​​speckle pattern generation, developments in RNGs and physical deduplication of photons often rely on the inherent randomness of materials such as the natural texture of paper, such that speckle patterns are generated under consistent light. For example, optical waveguides have demonstrated the ability to generate random numbers at Mbit / s speeds with verified randomness.

[0014] They have also developed an all-optical physical deduplication function (Fratalocchi et al.) based on speckle patterns in aerogel to achieve secure key generation. However, such a technique involves passing a laser beam through a volumetric scattering medium to capture a static speckle pattern. Although this approach is innovative, it may pose new challenges for cost-effective and fast random number generation. In addition, despite the improved security features such RNGs offer, their application in industrial settings is limited. The main obstacle is the high cost of sample fabrication and acquisition speed. Increasing these costs generally leads to increased purchasing speed.

[0015] Conversely, lower costs often come at the expense of slower speeds and more complex optimizations, creating a trade-off between these two problems. This creates a need for RNG systems that provide superior performance while also significantly reducing manufacturing costs.

[0016] As a conventional technique in the field of RNG, Patent Document 1 (Method and apparatus for ensuring continuity of random number output signal during von Neumann post-processing) is disclosed. The conventional technique discloses a solution to the discontinuity of the random number output signal, but the effect is that the system speed is improved and costs are reduced by storing random numbers in a memory buffer and outputting spare random numbers by another configuration when the stored random numbers are all consumed.

[0017] However, the prior art only discloses a random number output technique using von Neumann post-processing, and does not disclose anything about the application and binding potential of blood flow or blood samples. [Prior art documents] [Patent documents]

[0018] [Patent Document 1] Korean Patent No. 10-1925787 Summary of the Invention [Problem to be solved by the invention]

[0019] The present invention has been devised to solve the above-mentioned problems, and has an object to provide a system that is capable of generating random numbers at high speed while maximizing cost efficiency and improving the randomness of the generated random numbers, thereby ensuring the reliability of the generated random numbers.

[0020] However, the technical problems that the present invention aims to solve are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description of the invention described below. [Means for solving the problem]

[0021] A system according to one aspect of the present invention is a system for generating random numbers based on a blood sample, and includes a sample loading module that supplies a predetermined blood sample, a light irradiation module that irradiates light onto the blood sample provided by the sample loading module, and in this case, irradiates light onto the blood sample moved to a predetermined region of interest (ROI), an image generation module that uses the light irradiation module to capture an image of the blood sample located in the ROI in a predetermined manner to obtain an original speckle image, and a random number generation module that processes the original speckle image generated from the image generation module in a predetermined manner to generate a random number.

[0022] Preferably, the random number generation module may include a binary image generation unit that performs a binarization process to classify pixel values ​​constituting the original speckle image into bits of 0 or 1 to generate a binary image.

[0023] Preferably, the random number generation module may further include a von Neumann extraction unit that generates a first processed image by applying a von Neumann post-processing method to the binary image obtained by the binary image generation unit.

[0024] Preferably, the von Neumann extraction unit generates a second processed image by further applying a 2P-TO-VN method to the first processed image.

[0025] Preferably, the von Neumann extraction unit applies a One-Pass Tuple-Output von Neumann (1P-TO-VN) scheme to process two bits as one set in a preset scheme, and the preset scheme may be a scheme to remove a set if the set is a duplication of the same bit.

[0026] Preferably, the von Neumann extraction unit may be a scheme in which, when a set is a duplication of the same bits and, when the set is removed, a set is formed of different bits based on the remaining bits, the von Neumann extraction unit maintains only the first or second bit of the first and second bits constituting the set.

[0027] Preferably, the von Neumann extraction unit further groups the removed sets into quads according to the 1P-TO-VN scheme, the quads being composed of a first set and a second set, and may determine whether to continue removing the first and second sets based on whether the first and second sets are identical.

[0028] Preferably, the von Neumann extraction unit may further reconstruct the first and second sets if the first and second sets are different from each other.

[0029] Preferably, the random number generation module further includes an image scrambling unit that performs image scrambling on the binary image generated by the binary image generation unit to generate a preprocessed image, and the von Neumann extraction unit may generate first and second processed images from the preprocessed image generated by the image scrambling unit.

[0030] The present invention also provides a method for generating a random number based on a blood sample, the method including: (a1) a step of supplying a predetermined blood sample by a sample loading module; (a2) a step of irradiating light onto the blood sample provided by the sample loading module by a light irradiation module, in which case the blood sample moved to a predetermined region of interest is irradiated with light; (a3) ​​a step of capturing an image of the blood sample located in the region of interest in a predetermined manner using the light irradiation module to obtain an original speckle image; and (a4) a step of processing the original speckle image generated in step (a3) ​​in a predetermined manner to generate a random number.

[0031] Preferably, the step (a4) may include the steps of: (a41) performing a binarization process for classifying pixel values ​​constituting the original speckle image into bits of 0 or 1 to generate a binary image; (a42) generating a first processed image by applying a von Neumann post-processing method to the binary image obtained in the step (a41); and (a43) generating a second processed image by further applying a 2P-TO-VN method to the first processed image generated in the step (a42).

[0032] Preferably, the method may further include, before step (a42), a step (a42-0) of performing image scrambling on the binarized image generated in step (a41) to generate a pre-processed image. Effect of the Invention

[0033] According to one embodiment of the present invention, an innovative system is presented that utilizes blood flow to generate speckle patterns.

[0034] In particular, the inherent characteristics of blood, which is rich in diverse cellular components, can increase the complexity and entropy of the generated speckle, thereby increasing the randomness of the generated random numbers.

[0035] Because the present invention can capture speckle patterns at the rapid speed of the system and requires only a very small amount of blood, this can minimize both the cost and acquisition time associated with random number generation, improving system accessibility.

[0036] In addition, a system according to an embodiment of the present invention can increase the number of generated bits while providing overall unpredictability and strong security of the system by applying the 2P-TO-VN method, which is designed to ensure that the generated random bits are not biased.

[0037] In addition, various different additional effects can be achieved by various embodiments of the present invention. Such various effects of the present invention will be described in detail in each embodiment, or the description of the effects that can be easily understood by a person skilled in the art will be omitted. [Brief description of the drawings]

[0038] The following drawings attached to the present invention illustrate preferred embodiments of the present invention and, together with the detailed description of the invention described below, serve to further understand the technical concept of the present invention. Therefore, the present invention should not be interpreted as being limited to the matters described in such drawings. [Figure 1] 1 is a block diagram showing the overall configuration of a system according to an embodiment of the present invention; [Diagram 2] FIG. 1 is a schematic diagram illustrating a blood sample testing device of a system according to one embodiment of the present invention. [Diagram 3] 3 is a schematic diagram showing an example of a sample loading module in the blood sample testing device of FIG. 2. [Figure 4] 1 is a schematic diagram illustrating a process of generating random numbers using a system according to an embodiment of the present invention. [Diagram 5] FIG. 2 is a block diagram illustrating a processing procedure in a Von Neumann extraction unit of a system according to an embodiment of the present invention. [Figure 6] 6 is an example for explaining the process of FIG. 5. [Figure 7] 2 is a flow chart of a method according to an embodiment of the present invention. [Figure 8] FIG. 8 is a detailed flowchart of step S40 in FIG. 7. [Figure 9] 13 is a graph comparing the speckle de-correlation times of the original speckle image (OSI), the first processed image (CVN method), and the second processed image (2P-TO-VN method). [Figure 10] 13 is a graph comparing the random number matrix characteristics of the original speckle image (OSI), the first processed image (CVN method), and the second processed image (2P-TO-VN method). [Figure 11]The results show the speckle bit generation rates of the first processed image (CVN method) and the second processed image (2P-TO-VN method) and each image. [Figure 12a] 1 is an exemplary image showing an original speckle image (OSI), a first processed image (CVN method), and a second processed image (2P-TO-VN method). [Figure 12b] 1 is an example image with image scrambling applied; [Figure 13] 1 is a table showing the results of NIST statistical randomness tests on random numbers generated using a system according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] Hereinafter, a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings. Before that, the terms and words used in the specification and claims should not be interpreted only in their ordinary or dictionary sense, but should be interpreted in the meaning and concept consistent with the technical idea of ​​the present invention based on the principle that the inventor can appropriately define the concept of the term in order to best describe his / her invention.

[0040] Since the present invention can be modified in various ways and can have various embodiments, a specific embodiment will be illustrated in the drawings and described in detail in the description for carrying out the invention. However, this is not intended to limit the present invention to a specific embodiment, but should be understood to include all modifications, equivalents, and alternatives within the spirit and technical scope of the present invention. Similar reference numerals are used for similar components in the description of each drawing.

[0041] Terms such as first, second, A, B, etc. are used to describe various components, but the components should not be limited by the terms. Terms are used only to distinguish one structural element from another. For example, a first element may be named a second element, and similarly, the second element may be named a first element, without departing from the scope of the invention. The term "and / or" includes a combination of multiple associated listed items or any of multiple associated listed items.

[0042] When a component is referred to as being "coupled" or "connected" to another component, it should be understood that it may be directly coupled or connected to the other component, but that there may be other components in between. Conversely, when a component is referred to as being "directly coupled" or "directly connected" to another component, it should be understood that there are no other components in between.

[0043] The terms used in this application are merely used to describe certain embodiments and are not intended to limit the present invention. A singular surface includes a plural expression unless the context clearly indicates otherwise. In this application, the terms "comprise" or "have" and the like specify the presence of features, numbers, steps, operations, components, parts, or combinations thereof described above in the specification, and should be understood as not precluding the presence or additional possibility of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0044] Unless otherwise defined, all terms, including technical or scientific terms, used herein have the same meaning as commonly understood by a person of ordinary skill in the art to which the present invention belongs. Terms defined in commonly used dictionaries should be interpreted to have a meaning consistent with the meaning they have in the context of the relevant art, and should not be interpreted in an idealized or overly formal sense unless expressly defined in this application.

[0045] 1 is a block diagram showing the overall configuration of a system according to an embodiment of the present invention, and a system according to an embodiment of the present invention will be described with reference to FIG.

[0046] The system 10 includes a sample loading module 400, a light illumination module 100, an image generation module 200, and a random number generation module 300, where the random number generation module 300 may be executed as a program stored in a processor included in the system.

[0047] FIG. 2 is a schematic diagram showing a blood sample testing device of a system according to an embodiment of the present invention, and FIG. 3 is a schematic diagram showing an example of a sample loading module in the blood sample testing device of FIG.

[0048] Please refer to both FIG. 2 and FIG.

[0049] The light irradiation module 100 may be configured to irradiate light onto a blood sample S. As an example, the blood in the blood sample S may include red blood cells R, platelets P, etc., as illustrated in Fig. 3. The light may be, but is not limited to, a laser having a certain range of wavelengths (e.g., 532 nm).

[0050] The blood sample S may be, but is not limited to, extracted from the rat and contained in distilled deionized water. The applicant expressly indicates that the protocol GIST-2019-015 is strictly adhered to and all officially approved methods comply with the ARRIVE guidelines for experimental reporting.

[0051] Specifically, the applicant collected blood samples from 12-13 week old male Sprague Dawley mice weighing between 250-280g by tail vein dissection, administering 1ml of blood via a 23G needle while the animals were anesthetized with isoflurane. Such blood collection was performed on a group of 11 mice, and samples were immediately stored in citrate tubes (Cat. #363083, 9NC 0.109M Buffered Trisodium Citrate, BD Vacutainer, USA) for subsequent experiments.

[0052] The image generating module 200 may be configured to acquire a speckle image of the blood sample S illuminated with light. In detail, the image generating module 200 may include an imaging unit 210, a first lens 220, an aperture 230, a second lens 240, a polarizing plate 250, a reflecting mirror 260, a collimator 270, and a coupler 280.

[0053] The imager 210 may be configured to capture and acquire a speckle image of the light-illuminated blood sample S. By way of example, but not limited to, the imager 210 may be a CMOS camera (Neo 5.5 sCMOS, Andor Technology Ltd., Belfast, UK). The CMOS camera operates at a high frame rate of 1250 frames per second with an exposure time of 0.8 ms to smoothly capture a speckle image with a resolution of 128×512 pixels for generating subsequent data.

[0054] The speckle image of the blood sample S may include a speckle pattern that is irregularly generated by interference that occurs when light is reflected from or transmitted through the blood sample S. As an example, a total of seven experimental data sets were constructed with 1000 frames, and in order to preprocess the experimental data, these were divided into eight partitions each consisting of 32 × 256 pixels, resulting in a total of 56 sets being used.

[0055] The first lens 200 may be an objective lens and may be disposed between the imaging unit 210 and a sample loading module 400 in which the blood sample S is disposed. The aperture 230 may be an aperture and may be disposed between the imaging unit 210 and the first lens 220. The second lens 240 may be a tube lens and may be disposed between the imaging unit 210 and the aperture 230.

[0056] The polarizer 250 may be disposed between the second lens 240 and the aperture 230 .

[0057] In this case, the aperture 230, the second lens 240, and the polarizing plate 250 may be provided to improve the contrast in the speckle image of the blood sample S acquired by the image capture unit 210.

[0058] The reflecting mirror 260 may be configured to reflect the light emitted from the light irradiation module 100 to make the light incident on the blood sample S. In this case, at least one reflecting mirror 260 may be provided.

[0059] A collimator 270 may be disposed between the sample loading module 400, in which the blood sample S is disposed, and the reflector 260.

[0060] The coupler 280 may be an optical coupler and may be disposed between the collimator 270 and the reflector 260 .

[0061] The sample loading module 400 may be configured to transfer the blood sample S. As an example, the sample loading module 400 may include a PDMS (Polydimethylsiloxane) material. The sample loading module 400 may include a main body 410, an inlet portion 420, a transfer channel 230, and an outlet portion 440.

[0062] The main body 410 may form the overall shape of the sample loading module 400. The inlet portion 420 may be a portion where the blood sample S is introduced into the main body 410.

[0063] The transfer channel 430 may be provided in the main body 410 and configured to transfer the blood sample S introduced through the injector 420. Here, the transfer channel 430 may be referred to as a "microchannel." After the light (e.g., laser light of λ=532 nm) irradiated by the light irradiation module 100 passes through the transfer channel 430, the ROI region may be captured by the first lens 220. Thereby, an "original speckle image" is obtained, and the original speckle image is configured to generate random bits by the random number generation module 300 described later.

[0064] To construct the transfer channel 430 as a microchannel, the channel can be designed with precise dimensions of 45 mm in length, 45 μm in height, and 1 mm in width by applying soft photolithography. As an example, the applicant fabricates a PDMS slab according to various standard processes, and the slab, which is made of PDMS Sylgard 184 A / B (Dow Corning Korea Ltd.), is bonded to a cover glass by oxygen plasma treatment.

[0065] The transfer channel 430 is composed of a pump, a solenoid valve, a three-way valve, and a vacuum generator integrated with a 50 ml syringe to account for dead volume. The pump uses a solenoid valve to regulate the flow and can control the sample collection at a constant volume of 200 μL in variable recovery mode.

[0066] The discharge section 440 may be a portion that discharges the blood sample S flowing in the transfer channel 430 to the outside of the main body 410. The pumping module 500 may include a pump 510, a transfer tube 520, a first valve 530, a second valve 540, and a dead volume chamber 550.

[0067] The pumping module 500 may be connected to the sample loading module 400 and configured to move the blood sample S toward the sample loading module 400 by vacuum pressure.

[0068] The pump 510 may be a vacuum pump that generates a vacuum pressure to move the blood sample S toward the main body 410 of the sample loading module 400. As an example, the pump 510 may be a syringe pump. The transfer tube 520 may be a tube through which the blood sample S is moved toward the main body 410 by the vacuum pressure generated by the pump 510.

[0069] The first valve 530 may be provided in the transfer tube 520 and configured to adjust the vacuum pressure applied to the body 410. For example, the first valve 530 may be a solenoid valve. The second valve 540 and the dead volume chamber 550 may be provided in the transfer tube 520 to buffer the movement of the blood sample S. In this case, the dead volume chamber 550 may be connected to the transfer tube 520 via the second valve 540. For example, the second valve 540 may be a three-way valve, and the dead volume chamber 550 may be a container formed of an empty space.

[0070] The random number generation module 300 is configured to generate random numbers by processing the original speckle image generated from the image generation module 200. For example, the random number generation module 300 may be implemented in the form of a CPU, a GPU, an AP, or a combination thereof having a calculation function, and may be provided with various types of memory such as a DRAM, a flash memory, an SSD, etc., as necessary.

[0071] The random number generation module 300 may include a binary image generation unit 310, a von Neumann extraction unit 320, and an image scrambling unit 330.

[0072] FIG. 4 is a schematic diagram illustrating a process of generating random numbers using a system according to an embodiment of the present invention, and FIG. 5 is a block diagram illustrating a processing process in a von Neumann extraction unit of the system according to an embodiment of the present invention.

[0073] The binary image generating unit 310 is configured to perform a binary process of classifying pixel values ​​constituting the original speckle image into bits of 0 or 1 to generate a binary image, i.e., convert the original speckle image into a binary image.

[0074] The image scrambling unit 330 is configured to perform scrambling on the binary coded image. SCI (Scrambling Code Injection) means injecting a scrambling code into an image. By scrambling the image before passing through the Von Neumann extraction unit 320, the bit generation speed can be improved.

[0075] The first and second processed images described below may be images generated after being scrambled by the image scrambling unit 330 .

[0076] The von Neumann extraction unit 320 generates a first processed image by applying the von Neumann post-processing method to the binarized image obtained by the binarized image generation unit 310. Then, the 2P-TO-VN method may be further applied to the first processed image to generate a second processed image.

[0077] Here, the von Neumann post-processing method can be understood as the 1P-TO-VN method in contrast to the 2P-TO-VN method, that is, the 2P-TO-VN method includes an additional pass in the 1P-TO-VN method.

[0078] FIG. 6 is an example for explaining the process in FIG. 5, and the method of the present invention is not limited to the method shown in FIG.

[0079] The von Neumann extraction unit 320 can process in a 2P-TO-VN manner by first passing through the 1P-TO-VN manner and then passing through an additional path.

[0080] Referring to Figure 6, a process of classifying two bits into one set is performed. Here, if a set is a duplication of the same bit, the set may be removed. For example, the set of "00" or "11" may be removed.

[0081] Next, if a set is a duplication of the same bit, and the set is removed and a set is formed with different bits based on the remaining bits, it can be set to a method of maintaining only the first or second bit of the first and second bits constituting the set, thus completing the 1P-TO-VN method. For example, if it is '01' or '10', only the first bit, such as the '0' of '01' or the '1' of '10', can be maintained.

[0082] Next, the von Neumann extractor 320 may be configured to perform an additional pass, thereby reducing the bias of the bits.

[0083] The 2P-TO-VN method further groups the sets removed by the 1P-TO-VN method into quads, each of which is composed of a first set and a second set, and may be understood to further undergo a process of determining whether to maintain the removal of the first and second sets based on whether the first and second sets are identical. Here, if the first and second sets are different from each other, the first and second sets may be further restored. For example, a discarded set with different beginnings and endings, such as "0011" or "1100", may be set to be further preserved.

[0084] While the 1P-TO-VN method and the known von Neumann (CVN) bias removal method greatly reduce the number of bits due to severe compression, the 2P-TO-VN method can provide a good balance between bit preservation and bias removal. The 2P-TO-VN method can be an excellent alternative to the CVN method for generating strong and usable encryption keys (random numbers) by re-evaluating the bits that were discarded earlier to generate a data volume that is more suitable for practical applications as shown in Figure 6.

[0085] FIG. 7 is a flowchart of a method according to an embodiment of the present invention, and FIG. 8 is a flowchart showing step S40 of FIG. 7 in detail.

[0086] The method will be described with reference to FIG. 7 and FIG. 8, but the description overlapping with the above will be omitted.

[0087] The method according to an embodiment of the present invention includes steps S10 to S40.

[0088] Step S10 is a step in which the sample loading module 400 supplies a preset blood sample.

[0089] Step S20 is a step in which the light irradiation module 100 irradiates light onto the blood sample provided by the sample loading module 400, in which case the light is irradiated onto the blood sample that has been moved to a pre-specified area of ​​interest.

[0090] Step S30 is a step of obtaining an original speckle image by using the light irradiation module 100 to capture an image of the blood sample located in the region of interest in a preset manner.

[0091] Step S40 is a step of processing the original speckle image generated in step S30 in a preset manner to generate a random number.

[0092] Here, step S40 may further include steps S41 to S43.

[0093] Step S41 is a step of performing a binarization process for classifying pixel values ​​constituting the original speckle image into bits of 0 or 1, thereby generating a binarized image.

[0094] Step S411 is a step of performing image scrambling on the binarized image generated in step S41 to generate a preprocessed image.

[0095] Step S42 is a step of generating a first processed image by applying the Von Neumann post-processing method to the binarized image acquired in step S41.

[0096] Step S43 is a step of generating a second processed image by further applying the 2P-TO-VN method to the first processed image generated in step S42.

[0097] Fig. 9 is a graph comparing the speckle anti-correlation times of the original speckle image (OSI), the first processed image (CVN method), and the second processed image (2P-TO-VN method). The present invention verifies the results of a method that generates random numbers based on blood flow.

[0098] Referring to FIG. 9, the decorrelation time is the main measure of the estimated randomness. The autocorrelation curves for 56 original speckle images compared to the actual and scrambled random bits are shown. The decorrelation time shows a large difference when comparing the original speckle images to the derived random bits. The average decorrelation time for the original speckle images is 3.05 seconds with a standard deviation of ±0.43, while the set of random bits post-processed with CVN (Classic von Neumann) images, CVN with scrambling, 2P-TO-VN scheme, and 2P-TO-VN scheme with scrambling images show remarkably consistent averages and standard deviations of 1.542±0.019, 1.538±0.018, 1.551±0.017, and 1.529±0.013, respectively. Such closely clustered values ​​indicate low or non-existent correlation between the 56 diverse images, signifying high randomness.

[0099] FIG. 10 is a graph comparing the random number matrix characteristics of the original speckle image (OSI), the first processed image (CVN method), and the second processed image (2P-TO-VN method).

[0100] A comparison of bit uniformity and correlation difference is illustrated with reference to FIG. 10. To address potential bias in the data, the bit uniformity of the original speckle images was examined with an average of 0.626 reflecting the distortion. After applying the 2P-TO-VN method, an exemplary bit uniformity of 0.500 was confirmed for both the case processed by the 2P-TO-VN method alone and the two data sets combined with the insertion of a scrambling code. 0.500 can be understood to indicate an ideal balance of "1" and "0" bits.

[0101] Further investigation using correlation analysis on the 56 original and processed speckle images, shown in (b) through (d), indicates that the processed data set achieved a much lower correlation value than the original images, approximately 2,270 times lower.

[0102] In addition, when comparing the two processed data sets, the 2P-TO-VN method including SCI processing showed a correlation value that was reduced by 18.34% compared to the images to which only the 2P-TO-VN method was applied, which indicates that the SCI processing further worsens the correlation.

[0103] In (c) and (d), correlation values ​​outside the diagonal region approaching zero indicate decreased correlation.

[0104] The specific correlation metric for the original speckle images was confirmed to be 0.01896 with a standard deviation of 0.016264, and the processed data showed a significant improvement with the 2P-TO-VN images having correlation values ​​of 0.000453 and 0.000343 and the 2P-TO-VN images with SCI producing much lower correlations of 0.000383 and 0.000296. Such results may strongly highlight the efficiency of the 2P-TO-VN method in achieving bit uniformity and minimizing correlation, and may verify the statistical reliability of the blood flow speckle-based random number generation.

[0105] FIG. 11 shows the speckle bit generation rates of the first processed image (CVN method) and the second processed image (2P-TO-VN method) and the results showing each image.

[0106] Referring to Fig. 11, (a) is a graph of the bit generation rate generated from the original speckle bits. The speckle bits consist of 8.1 million bits. The mean and standard deviation of each image are calculated to be 16.65±2.27, 23.37±0.49, 52.35±3.34, and 67.70±2.44, respectively.

[0107] They have been tested for unpredictability by the NIST randomness test, which Applicant performed using the National Institute of Standards and Technology Statistical Test Suite (NIST) to determine the quality of the randomness of the random bits.

[0108] Incidentally, the NIST tests are designed to quantitatively assess the randomness of a binary sequence, and the NIST test suite consists of 15 individual tests, each designed to quantitatively measure a different aspect of the randomness of a binary sequence.

[0109] Testing has included evaluations of frequency, block frequency, execution, LRO (longest run), continuity, approximate entropy and cumulative sums (Cusums). Applicant's evaluations included aggregating binary sequences from 56 distinct random bits to ensure adequate stream length for seven statistical tests.

[0110] Here, P(s) is the distribution of sample path lengths, s is the path length, τ is the delay time, l* is the transmission mean free path, and τo is the characteristic decay time of the medium.

[0111] The autocorrelation will have a value between 0 and 1, and as the time delay increases the value should drop closer to 0, meaning there is no further correlation compared to the first image. To confirm the generation of truly random bits influenced by biomimetics, the correlation between two consecutive images in a time series of speckle pattern images was measured at the 50% point as blood passed through the moving channel 430.

[0112]

number

[0113]

number

[0114] In addition, the correlation between images is examined using a correlation matrix. In order to ensure the objectivity of the comparison, the random bits obtained by the method of the present invention are quantified and matched to create a matrix of uniform bits for examination (e.g., original speckle image: 7.8 Mbits, CVN image: 1.02 Mbits, CVN with SCI: 1.8 Mbits, 2P-TO-VN image: 3.3 Mbits, 2P-TO-VN with SCI: 4.8 Mbits).

[0115]

number

[0116] FIG. 13 is a table showing the results of NIST statistical randomness tests on random numbers generated using a system according to one embodiment of the present invention, and the applicant can confirm that all of them passed.

[0117] FIG. 12a is an example image showing an original speckle image (OSI), a first processed image (CVN method), and a second processed image (2P-TO-VN method), and FIG. 12b is an example image to which image scrambling has been applied.

[0118] Referring to FIG. 12, the speckle pattern generates unpredictable random bits consisting of 0 and 1 bits, and additional randomness is integrated using image scrambling and 2P-TO-VN methods for higher power and improved performance.

[0119] Referring to (b), it can be seen that the total pixels of the images containing SCI are 49980 (60Х833) pixels (see (a)) and 56520 (60Х942) pixels (see (b)), respectively, and thus affected by scrambling.

[0120] As described above, the present invention has been described using limited examples, but the present invention is not limited thereto, and it is of course possible for a person having ordinary skill in the art to which the present invention pertains to make various modifications and variations within the technical spirit of the present invention and the equivalent scope of the claims described below. [Explanation of symbols]

[0121] 10: System 100: Light irradiation module 200: Image generation module 300: Random number generation module 400: Sample Loading Module

Claims

1. 1. A system for generating random numbers based on a blood sample, comprising: a sample loading module for supplying a pre-determined blood sample; a light irradiation module that irradiates light onto the blood sample provided by the sample loading module, the light irradiation module irradiating light onto the blood sample moved to a pre-specified region of interest (ROI); an image generating module for capturing an image of the blood sample located in the region of interest in a preset manner using the light illuminating module to obtain an original speckle image; a random number generation module that processes the original speckle image generated by the image generation module in a preset manner to generate random numbers; the random number generation module includes a binary image generation unit that performs a binary process for classifying pixel values ​​constituting the original speckle image into 0 or 1 bits and generates a binary image; The system, wherein the random number generation module further includes a von Neumann extraction unit that generates a first processed image by applying a von Neumann post-processing method to the binary image obtained by the binary image generation unit.

2. The von Neumann extraction unit A second processed image is generated by further applying a two-pass tuple-output von Neumann (2P-TO-VN) method to the first processed image. The system of claim 1 .

3. The von Neumann extraction unit Applying the 1P-TO-VN (One-Pass Tuple-Output von Neumann) method, Two bits are processed in a set in a preset manner, and the preset manner is a manner of removing a set if the set is a duplication of the same bit; The system of claim 2.

4. The von Neumann extraction unit When a set is a duplication of the same bit, and the set is composed of different bits based on the remaining bits after the set is removed, only the first or second bit of the first and second bits constituting the set is maintained; The system of claim 3.

5. The von Neumann extraction unit Further grouping the removed sets into quads according to the 1P-TO-VN method, the quads being composed of a first set and a second set, and determining whether to remove or keep the first and second sets based on the differences between the first and second sets; The system of claim 4.

6. The von Neumann extraction unit if the first and second sets are different from each other, further recovering the first and second sets; The system of claim 5.

7. The random number generation module includes: The method further includes an image scrambling unit that performs image scrambling on the binary image generated by the binary image generating unit to generate a preprocessed image, The von Neumann extraction unit A first and a second processed image are generated based on the preprocessed image generated by the image scrambling unit. The system of claim 2.

8. 1. A method for generating a random number from a blood sample, comprising: (a1) supplying a preset blood sample by a sample loading module; (a2) a step of irradiating light onto the blood sample provided by the sample loading module by a light irradiation module, the step of irradiating light onto the blood sample moved to a pre-designated region of interest; (a3) using the light illumination module to capture an image of the blood sample located in the region of interest in a preset manner to obtain an original speckle image; (a4) processing the original speckle image generated in step (a3) ​​in a preset manner to generate a random number; wherein step (a4) comprises: (a41) performing a binarization process for classifying pixel values ​​constituting the original speckle image into bits of 0 or 1 to generate a binarized image; (a42) generating a first processed image by applying a von Neumann post-processing scheme to the binarized image obtained in step (a41).

9. The step (a4) (a43) further including a step of generating a second processed image by further applying a 2P-TO-VN method to the first processed image generated in the step (a42); The method according to claim 8.

10. Before the step (a42), (a42-0) further comprising a step of performing image scrambling on the binarized image generated in the step (a41) to generate a preprocessed image; 10. The method of claim 9.

Images