CN117290659A - Data tracing method based on regression analysis - Google Patents
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Abstract
The invention discloses a data tracing method based on regression analysis. The method solves the problem that the existing tracing method needs more space to store tracing link information, and a correct tracing structure cannot be obtained when multiple data sets are mixed. Configuring tracing information, generating a key sequence, and encrypting the tracing information to obtain an encryption sequence; configuring a tracing data set, and based on binary property of a logistic function, associating the encrypted sequence data with the tracing data as an initial value to obtain an associated tracing data sequence; and inputting the associated traceability data, performing logistic regression calculation on the associated traceability data to obtain an encryption sequence, and decrypting according to the key sequence to obtain corresponding traceability information. According to the method, the trace data characteristics are extracted, the trace data distribution result is used for setting the corresponding association characteristics according to the binary characteristic of the logistic function, the trace data corresponding characteristic values are distributed in the whole trace data set, so that the relevant mapping relation in trace can be directly calculated, the mapping relation is not required to be stored specially, and offline trace can be performed.
Description
Technical Field
The invention relates to the technical field of information security, in particular to a regression analysis-based data tracing method.
Background
After the national data information security method is determined, increasing the data security supervision and management force becomes an important guideline for each enterprise. The data tracing is an indispensable position as one of technical means for determining a leakage chain, a leakage target and a responsible person after data leakage. However, the conventional tracing method requires more additional space to store tracing link information, and when multiple data sets from different sources are mixed, a correct tracing result cannot be obtained.
Disclosure of Invention
The invention mainly solves the problems that the existing tracing method needs more space to store tracing link information and a correct tracing structure cannot be obtained when multiple data sets are mixed, and provides a regression analysis-based data tracing method. The tracing data and tracing information are associated through feature calculation, so that the problems are remarkably improved.
The technical problems of the invention are mainly solved by the following technical proposal: a data tracing method based on regression analysis comprises,
configuring tracing information, generating a key sequence, and encrypting the tracing information to obtain an encryption sequence;
configuring a tracing data set, and based on binary property of a logistic function, associating the encrypted sequence data with the tracing data as an initial value to obtain an associated tracing data sequence;
and inputting the associated traceability data, performing logistic regression calculation on the associated traceability data, calculating an encryption sequence, and decrypting according to the key sequence to obtain corresponding traceability information.
According to the method, logistic regression calculation is adopted for tracing, tracing information corresponding to the method is needed, the tracing information is associated with tracing data according to the binary property of the logistic function, and tracing is carried out on the tracing data through the logistic regression calculation mode. According to the method, the trace data characteristics are extracted, the corresponding association characteristics are set according to the binary characteristics of the logistic function based on the trace data distribution condition, so that the trace data corresponding characteristic values are distributed in the whole trace data set, the relevant mapping relation in trace can be directly calculated, no special storage mapping relation is needed, and offline trace can be performed.
As a preferable scheme, binary conversion is carried out on the traceability information to obtain the traceability information sequence.
As a preferred scheme, for a traceable data set, each traceable data feature is extracted, and a feature extraction equation is constructed for each traceable data in a logic conversion form.
Let the trace-source dataset c= { k 1 ,k 2 ,k 3 ,…, k m Extracting each trace source data feature omega a, and constructing a feature extraction equation k (a) =omega a 1 +ωa 2 +ωa 3 …ωa N A represents a feature, ω is a feature coefficient, and N is a feature number.
As a preferred solution, the formula is a more general form of a logistic function based on the binary property of the logistic function df (t)/dt=τf (1-f (t)) (f (t) -c), where c is the rule threshold, f 0 Where f (t) is used to describe the proportion of corresponding samples in the system capacity determination, τ is a constant, when the system initial value f is equal to the value of f (t) at t=0 0 <c, f (t) converges to 0, when the system initial value f 0 >c, f (t) is converged to 1, and the encrypted sequence is sequentially used as an initial value omega of a feature extraction equation 0 And sets a classification function σ (f (t)):
ω 0 ∈(c,1],Z=1
ω 0 ∈[0,c),Z=0
z is the value of the classification function sigma, and the encryption sequence and the traceability data are associated.
In the scheme, the convergence form and the initial value f of the logistic function 0 The method comprises the steps of correlating, wherein c is an Allee threshold value, taking c=0.5, directly substituting an encryption sequence into a traceable data set, setting an initial value of a corresponding traceable information logistic function to be 1 if an encryption sequence element is larger than 0.5, setting the initial value of the corresponding traceable information logistic function to be 0 if the encryption sequence element is smaller than 0.5, and setting an initial value omega of a feature extraction equation to be 0 0 As initial values of the functions, the encryption sequence is sequentially used as initial values omega of the feature extraction equation 0 And after the correlation, forming a correlation tracing data sequence, and storing the position information of the correlation tracing data sequence element corresponding to the encryption sequence into a constant term of a feature extraction equation. Correlating the trace data, i.e. k (a) =ω 0 +ωa 1 +ωa 2 +ωa 3 …ωa N 。
As a preferred solution, the performing logistic regression calculation on the associated traceable data includes:
substituting the associated trace-source data sequence into a logistic equation f (x) =f 0 e τx /(1+ f 0 (e τx -1) iterative computation, simultaneously performing classification processing, and computing to obtain an encryption sequence. According to the property of the logistic equation, at an initial value f 0 When E (c, 1), f (x) converges to 1, initial value f 0 When=0, f (x) is always 0, and the initial value f 0 When=1, f (x) is always 1. The classification process is to divide f (x) converged to 1 into 1, divide f (x) converged to 0 into 0, and recover the encrypted sequence from the associated trace-source data.
As a preferred aspect, the key sequence obtaining includes:
and setting system capacity, setting equation initial values and control parameters according to a logistic mapping equation, and producing a randomly distributed sequence to obtain a key sequence.
As a preferable scheme, corresponding traceability information is obtained through decryption according to a key sequence, wherein the key sequence is obtained through substitution of initial values and control parameters which are the same as those of the key sequence for encrypting the traceability information into a logistic mapping equation for iteration. The decrypted key sequence adopts the same key sequence as the key sequence in encryption, namely the same initial value and the same control parameter are adopted, and the same key sequence is obtained by substituting the initial value and the control parameter into a logistic mapping equation for iteration.
As a preferred solution, the generating the key sequence encrypts the tracing information to obtain an encrypted sequence, which includes:
substituting the key sequence into the sigmod function for processing, and creating a classification function
P (z) = {1, z <0.5|0, z. Gtoreq.0.5 }, where z is the key sequence element,
and substituting the key sequence into the classification function to classify to obtain a classification sequence, and carrying out symmetric encryption on the traceability information based on the classification sequence to obtain an encryption sequence.
The random, initial value sensitive and uniformly distributed key sequences are generated based on the logistic mapping, the difficulty of acquiring the key sequences based on the statistical distribution characteristics is increased, and the security is higher.
Therefore, the invention has the advantages that: and extracting the trace data characteristics, setting corresponding association characteristics based on trace data distribution results and according to binary characteristics of a logistic function, so that the trace data corresponding characteristic values are distributed in the whole trace data set, the related mapping relation in trace can be directly calculated, no special storage mapping relation is needed, and offline trace can be performed. The traceability data constructed by the invention can acquire correct traceability information by adopting regression calculation, and solves the problem that a plurality of different source data can not acquire a correct traceability structure by mixing.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic image of the invention using logistic functions.
Detailed Description
The technical scheme of the invention is further specifically described below through examples and with reference to the accompanying drawings.
Examples:
the data tracing method based on regression analysis in this embodiment, as shown in fig. 1, includes:
s1, configuring tracing information, generating a key sequence, and encrypting the tracing information to obtain an encryption sequence;
s11, configuring tracing information, converting the tracing information into a binary form, and representing the binary form as follows: a= { x 1 ,x 2 ,x 3 ,…, x i }，x∈{0,1}；
S12, initializing the system capacity n, namely the iteration number and the initial value z 0 Control parameter μ, where z 0 Not {0,0.25,0.5,0.75,1.0}, μ ε [0,4 ]]Substituting the logistic mapping equation iteration,
z n+1 = μz n (1- z n )
when the control parameter mu takes 4, generating a completely random sequence distribution, and calculating to obtain a KEY sequence KEY= { z 1 ,z 2 ,z 3 ,…, z n }；
S13, substituting the key sequence into the sigmod function for processing, and creating a classification function
P (z) = {1, z <0.5|0, z. Gtoreq.0.5 }, where z is the key sequence element,
substituting the key sequence into the classification function to classify to obtain a classification sequence, and carrying out symmetric encryption on the traceability information based on the classification sequence to obtain an encryption sequence B= { y 1 ,y 2 ,y 3 ,…, y i }。
S2, configuring a tracing data set, and based on the binary property of a logistic function, associating the encrypted sequence data with the tracing data as an initial value to obtain an associated tracing data sequence;
s21, setting a traceable data set C= { k 1 ,k 2 ,k 3 ,…, k m Logit transformation of C sequences into forms of construction such as k (x) =ω 0 + ωx 1 +ωx 2 Conversion equation of +epsilon, epsilon is a constant, specifically extracting characteristic ωa, a representing characteristic of each trace data characteristic ω as characteristic coefficient, constructing characteristic extraction equation k (a) =ωa 1 +ωa 2 +ωa 3 …ωa N ；
S22, taking c=0.5 based on a logistic function df (t)/dt=τf (1-f (t)) (f (t) -c), wherein c is an Allee threshold value 0 When t=0, f (t) is a constant, and τ is a system initial value f as shown in fig. 2 0 <c, f (t) converges to 0, when the system initial value f 0 >c, f (t) converge to 1, according to whichProperties, i.e. setting the initial value ω for the feature extraction equation k (a) according to the encryption sequence B 0 ，k(a)= ω 0 +ωa 1 +ωa 2 +ωa 3 …ωa N ，
And sets a classification function σ (f (t)):
ω 0 ∈(c,1],Z=1
ω 0 ∈[0,c),Z=0
z is the value of the classification function sigma, and the association of the encryption sequence and the tracing data is completed to obtain an associated tracing data sequence D.
S3, inputting the associated traceability data, performing logistic regression calculation on the associated traceability data, calculating an encryption sequence, and decrypting according to the key sequence to obtain corresponding traceability information.
S31, substituting the associated traceable data sequence D into a logistic equation f (x) =f 0 e τx /(1+ f 0 (e τx -1) iterative computation, simultaneously performing classification processing, and computing to obtain an encryption sequence.
Specifically, the form ln (p/(1-p))=β of the known logic transformation equation 0 +β 1 X 1 +β 2 X 2 +…+β n X n ，β 0 For intercept, β is the respective variable coefficient, where p represents the probability that the trace-source data is related to trace-source information, so ln (p/(1-p))=ω 0 + ωx+ωx 2 +epsilon, and simultaneously performing exponential operation on two sides to obtain p=f 0 e τx /(1+ f 0 (e τx -1)), then the logistic equation f (x) =f 0 e τx /(1+ f 0 (e τx -1) the iterative operation result is p, according to the property of logistic equation f (x), at initial value f 0 When E (c, 1), f (x) converges to 1, initial value f 0 When=0, f (x) is always 0, and the initial value f 0 When=1, f (x) is always 1, and ω is known 0 That is, p is a value, and classification is performed such that f (x) converged to 1 is divided into 1, f (x) converged to 0 is divided into 0, and ω is known 0 The position information epsilon can be obtained according to the created form of the feature extraction equation, thereby recovering the encryption sequence B from the associated trace-source data.
S32, substituting the same system capacity, initial value and control parameter into a logistic mapping equation for iteration to obtain the same KEY sequence KEY;
s33, the obtained KEY sequence KEY is used for decoding the tracing information sequence B, and corresponding tracing information is obtained.
The present invention will be described below with reference to specific examples, which specifically include the following steps:
s1, configuring tracing information, generating a key sequence, and encrypting the tracing information to obtain an encryption sequence.
S11, configuring traceability information, such as { "trace": "from Protoss to Zerg", "author": "starCraft", "time": "2021-10-04 10:00:02" };
converting the traceability information into a binary form, and representing the traceability information as: a= { x 1 ,x 2 ,x 3 ,…, x i }，x∈{0,1}；
S12. initializing system capacity n=2048, and initial value z 0 Control parameter μ, where z 0 Not {0,0.25,0.5,0.75,1.0}, μ ε [0,4 ]]Substituting the iteration of the logstin mapping equation,
z n+1 = μz n (1- z n )
calculate KEY sequence key= { z 1 ,z 2 ,z 3 ,…, z n }；
S13, substituting the key sequence into the sigmod function for processing, and creating a classification function
P (z) = {1, z <0.5|0, z. Gtoreq.0.5 }, where z is the key sequence element,
substituting the key sequence into the classification function to classify to obtain a classification sequence, and carrying out symmetric encryption on the traceability information A based on the classification sequence to obtain an encryption sequence B= { y 1 ,y 2 ,y 3 ,…, y i }，y∈{0,1}。
S2, configuring a tracing data set, and based on the binary property of a logistic function, associating the encrypted sequence data with the tracing data as an initial value to obtain an associated tracing data sequence.
S21, constructing a character string tracing data set C= { k 1 ,k 2 ,k 3 ,…, k m K e { a, b, c, …, z }, sample size of 1000000;
extracting each stripTracing the data feature ωa, constructing a feature extraction equation k (a) =ωa 1 +ωa 2 +ωa 3 …ωa N ；
S22. Based on the logistic function df (t)/dt=τf (1-f (t)) (f (t) -c), a classification function σ (f (t)):
ω 0 ∈(c,1],Z=1
ω 0 ∈[0,c),Z=0
taking each element of the encryption sequence B as a convergence value of corresponding data in the traceable data set, namely taking the element of the encryption sequence B as an initial value of corresponding data of the traceable data set C after logic conversion to obtain k (a) =omega 0 +ωa 1 +ωa 2 +ωa 3 …ωa N And saving the position information of the data corresponding to the encryption sequence into a constant item epsilon of k (a), wherein the processing of the character string needs to consider reducing the change to the original data, and the set initial value selection blank character can be spliced to the tail part of the character string. And (5) associating the encryption sequence with the traceable data to obtain an associated traceable data sequence D.
S3, inputting the associated traceability data, performing logistic regression calculation on the associated traceability data, calculating an encryption sequence, and decrypting according to the key sequence to obtain corresponding traceability information.
S31, substituting the associated traceable data sequence D into a logistic equation f (x) =f 0 e τx /(1+ f 0 (e τx -1) performing iterative computation, and simultaneously performing classification processing, so as to obtain an encryption sequence B through computation;
s32, substituting the same system capacity, initial value and control parameter into a logistic mapping equation for iteration to obtain the same KEY sequence KEY;
s33, the obtained KEY sequence KEY is used for decoding the tracing information sequence B, and corresponding tracing information is obtained.
The specific embodiments described herein are offered by way of example only to illustrate the spirit of the invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions thereof without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Claims (8)
1. A regression analysis-based data tracing method is characterized by comprising the following steps of: comprising the steps of (a) a step of,
configuring tracing information, generating a key sequence, and encrypting the tracing information to obtain an encryption sequence;
configuring a tracing data set, and based on binary property of a logistic function, associating the encrypted sequence data with the tracing data as an initial value to obtain an associated tracing data sequence;
and inputting the associated traceability data, performing logistic regression calculation on the associated traceability data, calculating an encryption sequence, and decrypting according to the key sequence to obtain corresponding traceability information.
2. The regression analysis-based data tracing method of claim 1, wherein the tracing information is binary-converted to obtain a tracing information sequence.
3. The regression analysis-based data tracing method of claim 1, wherein for the tracing dataset, each tracing data feature is extracted, and a feature extraction equation is constructed for each tracing data in a logic conversion form.
4. A regression analysis-based data tracing method according to claim 2 or 3, wherein the regression analysis-based data tracing method is characterized by a logistic function df (t)/dt=τf (1-f (t)) (f (t) -c) dichotomy, where c is an Allee threshold, f 0 When t=0, the value of f (t), τ is a constant, and when the system initial value f 0 <c, the logistic function converges to 0, when the system initial value f 0 >c, converging the logistic function to 1, and taking the encrypted sequence as an initial value omega of a feature extraction equation in sequence 0 And sets a classification function σ (f (t)):
ω 0 ∈(c,1],Z=1
ω 0 ∈[0,c),Z=0
z is the value of the classification function sigma, and the encryption sequence and the traceability data are associated.
5. The regression analysis-based data tracing method of claim 4, wherein said performing logistic regression calculation on the associated tracing data comprises:
substituting the associated trace-source data sequence into a logistic equation f (x) =f 0 e τx /(1+ f 0 (e τx -1) iterative computation, simultaneously performing classification processing, and computing to obtain an encryption sequence.
6. A regression analysis-based data tracing method according to claim 1, 2 or 3, wherein the key sequence acquisition comprises:
and setting the system capacity, setting an equation initial value and a control parameter according to a logistic mapping equation, and performing iterative calculation to generate a random distribution sequence to obtain a key sequence.
7. The regression analysis-based data tracing method of claim 6, wherein the corresponding tracing information is obtained by decrypting a key sequence, wherein the key sequence is obtained by substituting initial values and control parameters identical to those of the key sequence for encrypting the tracing information into a logistic mapping equation.
8. The regression analysis-based data tracing method of claim 6, wherein the generating the key sequence encrypts the tracing information to obtain the encrypted sequence, comprising:
substituting the key sequence into the sigmod function for processing, and creating a classification function
P (z) = {1, z <0.5|0, z. Gtoreq.0.5 }, where z is the key sequence element,
and substituting the key sequence into the classification function to classify to obtain a classification sequence, and carrying out symmetric encryption on the traceability information based on the classification sequence to obtain an encryption sequence.
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