CN106354475A - High-performance random number generation method and generator - Google Patents
High-performance random number generation method and generator Download PDFInfo
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- CN106354475A CN106354475A CN201610761887.5A CN201610761887A CN106354475A CN 106354475 A CN106354475 A CN 106354475A CN 201610761887 A CN201610761887 A CN 201610761887A CN 106354475 A CN106354475 A CN 106354475A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
- G06F7/58—Random or pseudo-random number generators
- G06F7/588—Random number generators, i.e. based on natural stochastic processes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
- G06F7/58—Random or pseudo-random number generators
- G06F7/582—Pseudo-random number generators
- G06F7/586—Pseudo-random number generators using an integer algorithm, e.g. using linear congruential method
Abstract
The invention discloses a high-performance random number generation method. A physical data acquisition module generates random numbers with poor randomness, wherein the random numbers with poor randomness are original random numbers. The original random numbers are subjected to randomness algorithm processing to obtain true random numbers. The invention further provides a high-performance random number generator which comprises the physical data acquisition module and a randomness algorithm processing module, wherein the randomness algorithm processing module is used for performing algorithm transformation of the original random numbers from rational numbers to approximate to irrational numbers. During algorithm transformation, gradual fraction is firstly obtained and subjected to big integer division to obtain results, the results are subjected to binary expansion, and a high-randomness random sequence is finally obtained. According to the high-performance random number generation method and the generator, acquired data are processed to obtain high-performance random numbers, the random numbers with complete randomness can be continuously generated, and the generated high-performance random numbers meet all specified detection of randomness detection specifications of State Cryptography Administration.
Description
Technical field
The present invention relates to a kind of new randomizer, more particularly, to a kind of high-performance random number generation method and generation
Device.
Background technology
Random number plays an important role in the field such as Monte Carlo Calculation and information encryption.For example in cryptographic algorithm close
It is random number that key requires, and in addition the pilot process of many cipher protocols is also required to random number, such as bb84 agreement of quantum cryptography etc..
The quality of randomizer quality determines the safety of itself of password product.
Randomizer refers to produce equipment or the algorithm of random number.The quality of randomizer quality determines close
The height of the safety of code product.Preferably the generation of random number can regard the result throwing coin as, according to front side
Or reverse side is labeled as " 0 " or " 1 ", for throwing result each time, the probability that " 0 " or " 1 " occurs is 1/2.And throw knot
Separate between fruit, that is, before throwing result do not interfere with result below.In actual applications, produce by this way
Raw random number is clearly unpractical, but it can assess the randomizer of reality as a kind of module.
Modern randomizer generally uses physical method and produces true random number, does not use algorithm to produce pseudorandom
Number.Various the random physical process such as thermal noise of cosmic noise, circuit and radioactive decay all can be used to produce random physical letter
Number, for example occur as soon as the real random number generator as random signal source using corpuscular radiation source early in the seventies.With micro- electricity
The development that son is learned, the appearance of cheap high-quality IC chip so that the thermal noise of circuit become be easiest to obtain with
Machine physical signalling, therefore modern majority real random number generator is designed with the thermal noise of circuit as random signal source.
Although the random number being produced with physical method is true random number, in the middle of data acquisition and a/d conversion etc.
The error of process, the binary sequence finally obtaining tends not to specify by national Password Management office " randomness inspection criterion "
All 15 detections are it is necessary to just can obtain high performance random number after the data collecting is processed.Existing process
Method for example enters line translation using linear feedback shift register to the data collecting, or using chaos algorithm, data is entered
The methods such as line translation all can not be fully solved the problem generating high-performance random number.
Content of the invention
The technical problem to be solved is, for above-mentioned the deficiencies in the prior art, provides a kind of high-performance random
Number method for generation and generator, this high-performance random number generation method and generator are processed to the data collecting, and obtain
High-performance random number, can be continuously generated the random number of completely random, and produced high-performance random number meets national password
All regulation detections in management board's " randomness inspection criterion ".
For solving above-mentioned technical problem, the technical solution used in the present invention is: high-performance random number generation method, its feature
It is to comprise the following steps:
First, the poor random number of randomness, the random number of described poor-performing are generated by physical data acquisition module
For original random number；
Secondly, by randomness algorithm processing module original random number is carried out randomness algorithm process obtain truly random
Number.
As further improved technical scheme of the present invention, described randomness algorithm processing module is carried out to original random number
Rational number approaches surd algorithmic transformation；Described algorithmic transformation obtains convergent first, then to convergent using big
Division of integer obtains result, then result is carried out binary expansion, finally gives the random sequences of high randomness.
As further improved technical scheme of the present invention, chip generation randomness is generated by pseudo random number preferably pseudo-
Random number；Step-by-step XOR is carried out to original random number and the good pseudo random number of randomness by randomness algorithm processing module,
Finally give the true random number of high randomness.
As further improved technical scheme of the present invention, described randomness algorithm processing module to original random number with
After the good pseudo random number of machine carries out step-by-step XOR, then XOR result step-by-step is negated, finally give the true of high randomness
Random number.
As further improved technical scheme of the present invention, described pseudo random number generation module is obtained using pseudo random number algorithm
To the good pseudo random number of randomness；Finally using psuedo random number bit position 0 or 1, step-by-step determines the physics collection corresponding ratio of number
Whether special position overturns, and finally gives the true random number of high randomness.
For realizing above-mentioned technical purpose, another kind of high-performance random number generation method that the present invention provides, walk including following
Rapid:
Physics collection random number first, random number poor for this randomness is intercepted with every s bit；Every s bit
Represent the binary system signless integer of a s position, successively as the part business x of continued fraction1,…,xn..., put x0=0；Infinitely connect
Fraction < x0..., xn... > necessarily converge on an irrational number ξ；
Because x0=0, thus the arithmetic point left side of irrational number ξ permanent be 0, decimally the binary expansion on point the right as with
Machine number；
Calculate each convergent of irrational number ξ with Do statement successively, use condition knkn+1≥2mAs loop termination
Condition；
After loop termination, using convergentIrrational number ξ can be deployed into m position after arithmetic point in a binary fashion；By
At most may continuously occur s 0 on the right of arithmetic point, irrational number ξ is deployed into s+m position after arithmetic point in a binary fashion, then
Leftmost s bit is intercepted, remaining m bit is as random number.
S, n, m can take arbitrarily long, and s takes 32 or 64, m to take between 1 ten thousand to 10 ten thousand；N is determined by calculating by m, meets
knkn+1≥2m.
For realizing above-mentioned technical purpose, present invention also offers a kind of high-performance randomizer, including for generating
The physical data acquisition module of the random number of random poor-performing, the random number of described poor-performing is original random number；Also wrap
Include and process to obtain the randomness algorithm processing module of true random number for original random number is carried out with randomness algorithm.
As further improved technical scheme of the present invention, described randomness algorithm processing module is used for original random number
Carry out rational number and approach surd algorithmic transformation；Described algorithmic transformation obtains convergent first, then convergent is made
Obtain result with big integer division, then result carries out binary expansion, finally gives the random sequences of high randomness.
As further improved technical scheme of the present invention, also include pseudo random number and generate chip, described pseudo random number life
Chip is become to be used for generating the preferable pseudo random number of randomness；Described randomness algorithm processing module be used for original random number with
The good pseudo random number of machine carries out step-by-step XOR, finally gives the true random number of high randomness.
As further improved technical scheme of the present invention, described randomness algorithm processing module to original random number with
After the good pseudo random number of machine carries out step-by-step XOR, then XOR result step-by-step is negated, finally give the true of high randomness
Random number；Described pseudo random number generation module obtains the good pseudo random number of randomness using pseudo random number algorithm；Finally use
Psuedo random number bit position 0 or 1, step-by-step determines whether the physics collection corresponding bit of number overturns, and finally gives high randomness
True random number.
The present invention carries out, using continued fraction method, the random number that rational approximations produce to physical method and carries out to irrational number
Process and produce high performance random number.The following is principle and the process step to the data collecting of the present invention.Straight herein
Connect the conclusion quoted and do not prove, refer to Hua Luogeng " number theory guiding " chapter 10.
Irrational number ξ=< x0, x1..., xn... >, wherein xiIt is integer, during i ＞ 0, xi＞ 0.Its convergent
Wherein hn, knMeet following recursive equation (1)
hn=xnhn-1+hn-2
kn=xnkn-1+kn-2
For any Integer n >=-1, define matrix
Wherein specifyUnder such regulation, recursive equation (1) is for any integer
N >=0 is all set up.
Above-mentioned recursive equation can be expressed in matrix as: for any Integer n >=0,
Prove: according to matrix multiplication rule, we obtain
According to recursive equation (1), it is equal on the right of above formulaBecauseBy mnAnd mn-1
Substitute into equation (3), obtain equation (2).
Can prove that for any Integer n >=0,
Prove: proved with inductive method.As n=0, according to (2) formula,Formula is set up.Assume n
During=k, (4) formula is set up,During so n=k+1, according to (2) formula,So (4) formula is also set up during n=k+1.
Determinant is taken to obtain on (4) formula both sides,
Obtain h by after determinantal expansionnkn-1-hn-1kn=(-
1)n+1(5), so for any Integer n >=0, greatest common divisor gcd (hn, kn)=1, therefore each convergentBoth it is all
About fraction.May certify that unlimited simple continued fraction < x0, x1..., xn... > and (wherein xiIt is integer, during i ＞ 0, xi＞ 0) certain
Converge on an irrational number.For any Integer n >=0, the error estimation inequality that irrational number ξ is approached with its convergent
The present invention's is to be deployed into irrational number in a binary fashion after arithmetic point to specify digit to obtain high-performance random
Number.The following is concrete steps:
According to (6), as long as meetingThenUsing convergentCan by irrational number ξ with
Binary mode is deployed into m position after arithmetic point.This condition equivalence is in knkn+1≥2m(7).In Project Realization, physics will be used first
The random number that the randomness of method generation is poor is intercepted with every s bit.Every s bit represents the binary system of a s position no
Symbol integer, successively as the part business x of continued fraction1,…,xn..., we put x0=0.Infinite continued fraction < x0..., xn...
> necessarily converge on an irrational number ξ.Because x0=0, so the arithmetic point left side perseverance of irrational number ξ is 0, we decimally put the right side
The binary expansion on side is as random number.Calculate each convergent of irrational number ξ with Do statement successively, made with condition (7)
Condition for loop termination.After loop termination, using convergentIrrational number ξ can be deployed into decimal in a binary fashion
M position after point.In view of at most may continuously occur s 0 on the right of arithmetic point, irrational number ξ is deployed into by a binary fashion
S+m position after arithmetic point, more leftmost s bit is intercepted, remaining m bit is as random number.We are entered with software approach
During row Proof-Of Principle, the value of s takes the value of 64, m to take 106.Finally giving length is 106Binary sequence as random number.We
Generating multigroup length on computer in this way is 106Binary sequence, all passed through national Password Management office " random
Property inspection criterion " specify all 15 detection.
The present invention is based on theory of continued-fractions, carries out reasonable forcing using the convergent that irrational number continued fraction is launched to irrational number
Recently produce random number.As long as the random number being produced with physical method is true random number, even if the randomness of random number source is very poor,
Also can obtain after being processed with the method for the present invention by owning that national Password Management office " randomness inspection criterion " specifies
The high-performance random number of 15 detections.
Brief description
Fig. 1 is generating random number schematic flow sheet.
Fig. 2 is randomness handling process schematic diagram.
Fig. 3 is the result schematic diagram of the present invention.
Fig. 4 is that the present invention obtains the first structural representation.
Fig. 5 is the second structural representation of the present invention.
Specific embodiment
Embodiment 1
Referring to Fig. 1, Fig. 2, Fig. 3 and Fig. 4, this high-performance random number generation method, comprise the following steps:
First, the poor random number of randomness, the random number of described poor-performing are generated by physical data acquisition module
For original random number；
Secondly, by randomness algorithm processing module original random number is carried out randomness algorithm process obtain truly random
Number.
Preferably, described randomness algorithm processing module original random number is carried out rational number approach surd
Algorithmic transformation；Described algorithmic transformation obtains convergent first, then obtains result to convergent using big integer division, then
Result is carried out binary expansion, finally gives the random sequences of high randomness.
Or as shown in figure 5, chip is generated by pseudo random number generate the preferable pseudo random number of randomness；By randomness
Algorithm processing module carries out step-by-step XOR to original random number and the good pseudo random number of randomness, finally gives high randomness
True random number.Described randomness algorithm processing module carries out step-by-step XOR to original random number and the good pseudo random number of randomness
Afterwards, then XOR result step-by-step is negated, finally give the true random number of high randomness.Further, described pseudo random number life
Module is become to obtain the good pseudo random number of randomness using pseudo random number algorithm；Finally using psuedo random number bit position 0 or 1, press
Position determines whether the physics collection corresponding bit of number overturns, and finally gives the true random number of high randomness.
Embodiment 2
This high-performance random number generation method, comprises the following steps:
Physics collection random number first, random number poor for this randomness is intercepted with every s bit；Every s bit
Represent the binary system signless integer of a s position, successively as the part business x of continued fraction1,…,xn..., put x0=0；Infinitely connect
Fraction ＜ x0..., xn... ＞ necessarily converges on an irrational number ξ；
Because x0=0, thus the arithmetic point left side of irrational number ξ permanent be 0, decimally the binary expansion on point the right as with
Machine number；
Calculate each convergent of irrational number ξ with Do statement successively, use condition knkn+1≥2mAs loop termination
Condition；
After loop termination, using convergentIrrational number ξ can be deployed into m position after arithmetic point in a binary fashion；
Due at most may continuously occur s 0 on the right of arithmetic point, irrational number ξ is deployed into s+m position after arithmetic point in a binary fashion,
Again leftmost s bit is intercepted, remaining m bit is as random number.
S, n, m can take arbitrarily long, and s takes 32 or 64, m to take between 1 ten thousand to 10 ten thousand；N is determined by calculating by m, meets
knkn+1≥2m.Referring to Fig. 1, produce true random number chip output random number using physical characteristics, through randomness processing procedure,
Produce high-performance random number eventually.Referring to Fig. 2, after randomness processing procedure, approach irrational number algorithm, final output using big integer
High-performance random number.
Embodiment 3
Referring to Fig. 1, Fig. 2, Fig. 3 and Fig. 4, this high-performance randomizer, including for generating random poor-performing
The physical data acquisition module of random number, the random number of described poor-performing is original random number；Also include for original with
Machine number carries out randomness algorithm and processes to obtain the randomness algorithm processing module of true random number.Described randomness algorithm processes mould
Block approaches surd algorithmic transformation for carrying out rational number to original random number；Described algorithmic transformation obtains asymptotic point first
Number, then obtains result to convergent using big integer division, then result carries out binary expansion, finally gives high randomness
Random sequences.
Or as shown in figure 5, also including pseudo random number to generate chip, described pseudo random number generates chip and is used for generating at random
The preferable pseudo random number of property；Described randomness algorithm processing module is used for original random number and the good pseudo random number of randomness
Carry out step-by-step XOR, finally give the true random number of high randomness.Described randomness algorithm processing module to original random number with
After the good pseudo random number of randomness carries out step-by-step XOR, then XOR result step-by-step is negated, finally give high randomness
True random number.Described pseudo random number generation module obtains the good pseudo random number of randomness using pseudo random number algorithm；Finally make
With psuedo random number bit position 0 or 1, whether the step-by-step decision physics collection corresponding bit of number overturns, and finally gives high randomness
True random number.
Claims (10)
1. a kind of high-performance random number generation method is it is characterised in that comprise the following steps:
First, the poor random number of randomness is generated by physical data acquisition module, the random number of described poor-performing is former
Beginning random number；
Secondly, randomness algorithm process is carried out by randomness algorithm processing module to original random number and obtain true random number.
2. high-performance random number generation method according to claim 1 it is characterised in that:
Described randomness algorithm processing module carries out rational number to original random number and approaches surd algorithmic transformation；Described algorithm
Conversion obtains convergent first, then obtains result to convergent using big integer division, then result is carried out binary system
Launch, finally give the random sequences of high randomness.
3. high-performance random number generation method according to claim 1 it is characterised in that:
Chip is generated by pseudo random number and generates the preferable pseudo random number of randomness；By randomness algorithm processing module to original
Random number and the good pseudo random number of randomness carry out step-by-step XOR, finally give the true random number of high randomness.
4. high-performance random number generation method according to claim 3 it is characterised in that: described randomness algorithm processes mould
Block carries out after step-by-step XOR to original random number and the good pseudo random number of randomness, then XOR result step-by-step is negated,
Obtain the true random number of high randomness eventually.
5. the high-performance random number generation method according to claim 3 or 4 it is characterised in that: described pseudo random number generates
Module obtains the good pseudo random number of randomness using pseudo random number algorithm；Finally using psuedo random number bit position 0 or 1, step-by-step
Determine whether the physics collection corresponding bit of number overturns, finally give the true random number of high randomness.
6. a kind of high-performance random number generation method is it is characterised in that comprise the following steps:
Physics collection random number first, random number poor for this randomness is intercepted with every s bit；Every s bit represents
The binary system signless integer of one s position, successively as the part business x of continued fraction1,…,xn..., put x0=0；Infinite continued fraction
<x0..., xn... > necessarily converge on an irrational number ξ；
Because x0=0, so the arithmetic point left side perseverance of irrational number ξ is 0, decimally the binary expansion on point the right is as random number；
Calculate each convergent of irrational number ξ with Do statement successively, use condition knkn+1≥2mCondition as loop termination；
After loop termination, using convergentIrrational number ξ can be deployed into m position after arithmetic point in a binary fashion；Due to little
At most may continuously occur s 0 on the right of several points, irrational number ξ is deployed into s+m position after arithmetic point in a binary fashion, then will
The s bit on the left side intercepts, and remaining m bit is as random number.
S, n, m can take arbitrarily long, and s takes 32 or 64, m to take between 1 ten thousand to 10 ten thousand；N is determined by calculating by m, meets knkn+1
≥2m.
7. a kind of high-performance randomizer it is characterised in that: include the thing of the random number for generating random poor-performing
Reason data acquisition module, the random number of described poor-performing is original random number；Also include for original random number is carried out with
Machine algorithm process is to obtain the randomness algorithm processing module of true random number.
8. high-performance randomizer according to claim 7 it is characterised in that:
Described randomness algorithm processing module approaches surd algorithmic transformation for carrying out rational number to original random number；Described
Algorithmic transformation obtains convergent first, then obtains result to convergent using big integer division, then result carries out two and enters
System is launched, and finally gives the random sequences of high randomness.
9. high-performance randomizer according to claim 7 it is characterised in that:
Also include pseudo random number and generate chip, described pseudo random number generates chip and is used for generating the preferable pseudo random number of randomness；
Described randomness algorithm processing module is used for carrying out step-by-step XOR to original random number and the good pseudo random number of randomness, finally
Obtain the true random number of high randomness.
10. high-performance randomizer according to claim 9 it is characterised in that: described randomness algorithm processes mould
Block carries out after step-by-step XOR to original random number and the good pseudo random number of randomness, then XOR result step-by-step is negated,
Obtain the true random number of high randomness eventually；It is good that described pseudo random number generation module obtains randomness using pseudo random number algorithm
Pseudo random number；Finally using psuedo random number bit position 0 or 1, step-by-step determines whether the physics collection corresponding bit of number overturns,
Obtain the true random number of high randomness eventually.
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| CN106980490A (en) * | 2017-05-10 | 2017-07-25 | 李昀芊 | A kind of real random number generator based on fluid molecule Brownian movement |
| CN108923919A (en) * | 2018-07-18 | 2018-11-30 | 安徽问天量子科技股份有限公司 | The control method and control system for selecting base device of the sub- cryptographic system of base unit weight are selected partially |
| CN109783059A (en) * | 2018-12-28 | 2019-05-21 | 武汉船舶通信研究所（中国船舶重工集团公司第七二二研究所） | A kind of quantum random number production method and device |
| CN113377338A (en) * | 2021-07-09 | 2021-09-10 | 广东电网有限责任公司 | Self-feedback true random number generation method and system based on power Internet of things |
| CN114579082A (en) * | 2022-05-06 | 2022-06-03 | 北京中科国光量子科技有限公司 | Quantum random number generator based on laser phase noise |
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| CN114579082A (en) * | 2022-05-06 | 2022-06-03 | 北京中科国光量子科技有限公司 | Quantum random number generator based on laser phase noise |
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Application publication date: 20170125 |